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Barbanti L, Hothorn T. A transformation perspective on marginal and conditional models. Biostatistics 2024; 25:402-428. [PMID: 36534895 DOI: 10.1093/biostatistics/kxac048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 11/02/2022] [Accepted: 11/28/2022] [Indexed: 08/04/2023] Open
Abstract
Clustered observations are ubiquitous in controlled and observational studies and arise naturally in multicenter trials or longitudinal surveys. We present a novel model for the analysis of clustered observations where the marginal distributions are described by a linear transformation model and the correlations by a joint multivariate normal distribution. The joint model provides an analytic formula for the marginal distribution. Owing to the richness of transformation models, the techniques are applicable to any type of response variable, including bounded, skewed, binary, ordinal, or survival responses. We demonstrate how the common normal assumption for reaction times can be relaxed in the sleep deprivation benchmark data set and report marginal odds ratios for the notoriously difficult toe nail data. We furthermore discuss the analysis of two clinical trials aiming at the estimation of marginal treatment effects. In the first trial, pain was repeatedly assessed on a bounded visual analog scale and marginal proportional-odds models are presented. The second trial reported disease-free survival in rectal cancer patients, where the marginal hazard ratio from Weibull and Cox models is of special interest. An empirical evaluation compares the performance of the novel approach to general estimation equations for binary responses and to conditional mixed-effects models for continuous responses. An implementation is available in the tram add-on package to the R system and was benchmarked against established models in the literature.
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Affiliation(s)
- Luisa Barbanti
- Institut für Epidemiologie, Biostatistik und Prävention, Universität Zürich, Hirschengraben 84, CH-8001 Zürich, Switzerland
| | - Torsten Hothorn
- Institut für Epidemiologie, Biostatistik und Prävention, Universität Zürich, Hirschengraben 84, CH-8001 Zürich, Switzerland
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2
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Debaly ZM, Truquet L. Multivariate time series models for mixed data. BERNOULLI 2023. [DOI: 10.3150/22-bej1474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Zinsou-Max Debaly
- ENSAI-CREST UMR 9194, Campus de Ker-Lann, Rue Blaise Pascal – BP 37203, 35712 BRUZ cedex, France
| | - Lionel Truquet
- ENSAI-CREST UMR 9194, Campus de Ker-Lann, Rue Blaise Pascal – BP 37203, 35712 BRUZ cedex, France
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3
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Morales-Navarrete D, Bevilacqua M, Caamaño-Carrillo C, Castro LM. Modelling Point Referenced Spatial Count Data: A Poisson Process Approach. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2140053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Affiliation(s)
- Diego Morales-Navarrete
- Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus Center for the Discovery of Structures in Complex Data, Chile
| | - Moreno Bevilacqua
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Viña del Mar, Chile
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Ca’ Foscari University of Venice, Italy
| | | | - Luis M. Castro
- Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus Center for the Discovery of Structures in Complex Data, Chile
- Centro de Riesgos y Seguros UC, Pontificia Universidad Católica de Chile, Santiago, Chile
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4
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Cerqueti R, Cesarone F, Heusch MC, Mottura CD. A new family of modified Gaussian copulas for market consistent valuation of government guarantees. REVIEW OF MANAGERIAL SCIENCE 2022. [PMCID: PMC9595587 DOI: 10.1007/s11846-022-00600-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
This paper deals with a copula-based stochastic dependence problem in the context of financial risks. We discuss the financial framework for assessing the theoretical up-front value of government guarantees on bank liabilities. EU States widely use these contracts to improve the financial system’s stability and manage the banking sector in crisis situations; in Italy, they have also been used to address the consequences of the Covid-19 emergency. From a market viewpoint, we deal with a defaultable guarantee contract where the State-guarantor and the bank-borrower are both subject to default risk, and their risks are interconnected. We show that the classical Gaussian copula is not satisfactory for modeling the dependence among the considered risks. Indeed, using the benchmark market model for credit risk portfolio management, we highlight some contradictory results observed for the up-front values of the guarantee when the default intensity of the guarantor is smaller than that of the borrower. Then, we introduce a new family of modified Gaussian copulas that overcomes the limitations of the standard approach, allowing to determine realistic results in terms of the guarantees “mark-to-model” value when the benchmark market model does not work. Numerical simulations validate the theoretical proposal.
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Affiliation(s)
- Roy Cerqueti
- Department of Social and Economic Sciences, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy
- School of Business, London South Bank University, 103 Borough Rd, London, SE1 0AA UK
- GRANEM, Universitè d’Angers, 49036 Angers CEDEX 01, France
| | - Francesco Cesarone
- Department of Business Studies, Roma Tre University, Via Silvio D’Amico, 77, 00145 Rome, Italy
| | - Maria C. Heusch
- Department of Business Studies, Roma Tre University, Via Silvio D’Amico, 77, 00145 Rome, Italy
| | - Carlo D. Mottura
- Department of Business Studies, Roma Tre University, Via Silvio D’Amico, 77, 00145 Rome, Italy
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5
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Nikoloulopoulos AK. Efficient and feasible inference for high-dimensional normal copula regression models. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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6
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A class of random fields with two-piece marginal distributions for modeling point-referenced data with spatial outliers. TEST-SPAIN 2022. [DOI: 10.1007/s11749-021-00797-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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7
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Seal S, Ghosh D. MIAMI: mutual information-based analysis of multiplex imaging data. Bioinformatics 2022; 38:3818-3826. [PMID: 35748713 PMCID: PMC9344855 DOI: 10.1093/bioinformatics/btac414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/09/2022] [Accepted: 06/21/2022] [Indexed: 02/01/2023] Open
Abstract
MOTIVATION Studying the interaction or co-expression of the proteins or markers in the tumor microenvironment of cancer subjects can be crucial in the assessment of risks, such as death or recurrence. In the conventional approach, the cells need to be declared positive or negative for a marker based on its intensity. For multiple markers, manual thresholds are required for all the markers, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lacks robustness. RESULTS We present a new method where different marker intensities are viewed as dependent random variables, and the mutual information (MI) between them is considered to be a metric of co-expression. Estimation of the joint density, as required in the traditional form of MI, becomes increasingly challenging as the number of markers increases. We consider an alternative formulation of MI which is conceptually similar but has an efficient estimation technique for which we develop a new generalization. With the proposed method, we analyzed a lung cancer dataset finding the co-expression of the markers, HLA-DR and CK to be associated with survival. We also analyzed a triple negative breast cancer dataset finding the co-expression of the immuno-regulatory proteins, PD1, PD-L1, Lag3 and IDO, to be associated with disease recurrence. We demonstrated the robustness of our method through different simulation studies. AVAILABILITY AND IMPLEMENTATION The associated R package can be found here, https://github.com/sealx017/MIAMI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Souvik Seal
- Department of Biostatistics and Informatics, University of Colorado CU Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado CU Anschutz Medical Campus, Aurora, CO 80045, USA
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8
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Mahmoudi MR. Evaluating the relationship between two periodically correlated processes with Mandelbrot-Van Ness fractional Brownian motion errors using periodic copula. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2091567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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9
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Sheikhi A, Arad F, Mesiar R. A heteroscedasticity diagnostic of a regression analysis with copula dependent random variables. BRAZ J PROBAB STAT 2022. [DOI: 10.1214/22-bjps532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Ayyub Sheikhi
- Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Fereshteh Arad
- Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Radko Mesiar
- Department of Mathematics and Descriptive Geometry, Faculty of Civil Engineering, STU Bratislava, Slovakia
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10
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Ma Z, Davis SW, Ho YY. Flexible copula model for integrating correlated multi-omics data from single-cell experiments. Biometrics 2022. [PMID: 35622236 DOI: 10.1111/biom.13701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 05/18/2022] [Indexed: 11/27/2022]
Abstract
With recent advances in technologies to profile multi-omics data at the single-cell level, integrative multi-omics data analysis has been increasingly popular. It is increasingly common that information such as methylation changes, chromatin accessibility, and gene expression are jointly collected in a single-cell experiment. In biomedical studies, it is often of interest to study the associations between various data types and to examine how these associations might change according to other factors such as cell types and gene regulatory components. However, since each data type usually has a distinct marginal distribution, joint analysis of these changes of associations using multi-omics data is statistically challenging. In this paper, we propose a flexible copula-based framework to model covariate-dependent correlation structures independent of their marginals. In addition, the proposed approach could jointly combine a wide variety of univariate marginal distributions, either discrete or continuous, including the class of zero-inflated distributions. The performance of the proposed framework is demonstrated through a series of simulation studies. Finally, it is applied to a set of experimental data to investigate the dynamic relationship between single-cell RNA-sequencing, chromatin accessibility, and DNA methylation at different germ layers during mouse gastrulation. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zichen Ma
- Department of Public Health Sciences, Clemson University, Clemson, SC, USA
| | - Shannon W Davis
- Department of Biological Sciences, University of South Carolina, Columbia, SC, USA
| | - Yen-Yi Ho
- Department of Statistics, University of South Carolina, Columbia, SC, USA
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11
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Safety and long-term improvement of mesenchymal stromal cell infusion in critically COVID-19 patients: a randomized clinical trial. Stem Cell Res Ther 2022; 13:122. [PMID: 35313959 PMCID: PMC8935270 DOI: 10.1186/s13287-022-02796-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 02/20/2022] [Indexed: 01/08/2023] Open
Abstract
Background COVID-19 is a multisystem disease that presents acute and persistent symptoms, the postacute sequelae (PASC). Long-term symptoms may be due to consequences from organ or tissue injury caused by SARS-CoV-2, associated clotting or inflammatory processes during acute COVID-19. Various strategies are being chosen by clinicians to prevent severe cases of COVID-19; however, a single treatment would not be efficient in treating such a complex disease. Mesenchymal stromal cells (MSCs) are known for their immunomodulatory properties and regeneration ability; therefore, they are a promising tool for treating disorders involving immune dysregulation and extensive tissue damage, as is the case with COVID-19. This study aimed to assess the safety and explore the long-term efficacy of three intravenous doses of UC-MSCs (umbilical cord MSCs) as an adjunctive therapy in the recovery and postacute sequelae reduction caused by COVID-19. To our knowledge, this is one of the few reports that presents the longest follow-up after MSC treatment in COVID-19 patients. Methods This was a phase I/II, prospective, single-center, randomized, double-blind, placebo-controlled clinical trial. Seventeen patients diagnosed with COVID-19 who require intensive care surveillance and invasive mechanical ventilation—critically ill patients—were included. The patient infusion was three doses of 5 × 105 cells/kg UC-MSCs, with a dosing interval of 48 h (n = 11) or placebo (n = 6). The evaluations consisted of a clinical assessment, viral load, laboratory testing, including blood count, serologic, biochemical, cell subpopulation, cytokines and CT scan. Results The results revealed that in the UC-MSC group, there was a reduction in the levels of ferritin, IL-6 and MCP1-CCL2 on the fourteen day. In the second month, a decrease in the levels of reactive C-protein, D-dimer and neutrophils and an increase in the numbers of TCD3, TCD4 and NK lymphocytes were observed. A decrease in extension of lung damage was observed at the fourth month. The improvement in all these parameters was maintained until the end of patient follow-up. Conclusions UC-MSCs infusion is safe and can play an important role as an adjunctive therapy, both in the early stages, preventing severe complications and in the chronic phase with postacute sequelae reduction in critically ill COVID-19 patients. Trial registration Brazilian Registry of Clinical Trials (ReBEC), UTN code-U1111-1254-9819. Registered 31 October 2020—Retrospectively registered, https://ensaiosclinicos.gov.br/rg/RBR-3fz9yr Supplementary Information The online version contains supplementary material available at 10.1186/s13287-022-02796-1.
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12
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Ribeiro VSO, Nobre JS, dos Santos JRS, Azevedo CLN. Beta rectangular regression models to longitudinal data. BRAZ J PROBAB STAT 2021. [DOI: 10.1214/21-bjps511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Vinícius S. O. Ribeiro
- Departamento de Estatística e Matemática Aplicada, Universidade Federal do Ceará, Brazil
| | - Juvêncio S. Nobre
- Departamento de Estatística e Matemática Aplicada, Universidade Federal do Ceará, Brazil
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13
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Petterle RR, Taconeli CA, da Silva JLP, da Silva GP, Laureano HA, Bonat WH. Unit gamma mixed regression models for continuous bounded data. J STAT COMPUT SIM 2021. [DOI: 10.1080/00949655.2021.1970164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Ricardo R. Petterle
- Department of Integrative Medicine, Paraná Federal University, Curitiba, Brazil
| | - César A. Taconeli
- Laboratory of Statistics and Geoinformation, Department of Statistics, Paraná Federal University, Curitiba, Brazil
| | - José L. P. da Silva
- Laboratory of Statistics and Geoinformation, Department of Statistics, Paraná Federal University, Curitiba, Brazil
| | - Guilherme P. da Silva
- Laboratory of Statistics and Geoinformation, Department of Statistics, Paraná Federal University, Curitiba, Brazil
| | - Henrique A. Laureano
- Laboratory of Statistics and Geoinformation, Department of Statistics, Paraná Federal University, Curitiba, Brazil
| | - Wagner H. Bonat
- Laboratory of Statistics and Geoinformation, Department of Statistics, Paraná Federal University, Curitiba, Brazil
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14
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Affiliation(s)
- Yisu Jia
- Department of Mathematics and Statistics, University of North Florida, Jacksonville, FL
| | | | | | - Robert Lund
- Department of Statistics, University of California, Santa Cruz, CA
| | - Vladas Pipiras
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC
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15
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Davis RA, Fokianos K, Holan SH, Joe H, Livsey J, Lund R, Pipiras V, Ravishanker N. Count Time Series: A Methodological Review. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1904957] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
| | | | - Scott H. Holan
- Department of Statistics, University of Missouri, Columbia, MO
- U.S. Census Bureau, Washington, DC
| | - Harry Joe
- Department of Statistics, University of British Columbia, Vancouver, Canada
| | | | - Robert Lund
- Department of Statistics, The University of California—Santa Cruz, Santa Cruz, CA
| | - Vladas Pipiras
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC
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16
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La Sorte FA, Horton KG. Seasonal variation in the effects of artificial light at night on the occurrence of nocturnally migrating birds in urban areas. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 270:116085. [PMID: 33234373 DOI: 10.1016/j.envpol.2020.116085] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 11/02/2020] [Accepted: 11/12/2020] [Indexed: 06/11/2023]
Abstract
Urban areas often contain large numbers of migratory bird species during seasonal migration, many of which are nocturnal migrants. How artificial light at night (ALAN) and urban landcover are associated with the diurnal occurrence of nocturnal migrants within urban areas across seasons has not been explored. Here, we use eBird bird occurrence information to estimate the seasonal species richness of nocturnally migrating passerines (NMP) within 333 well surveyed urban areas within the contiguous USA. We model the relationship between seasonal NMP species richness and ALAN, proportion of tree canopy cover, and proportion of impervious surface. NMP species richness reached its highest levels during spring and autumn migration and lowest during the winter and summer. Greater tree canopy cover was associated with higher NMP species richness during spring and autumn migration and the summer. A 10% increase in the proportion of tree canopy cover was associated with a 2.0% increase in NMP species richness during spring migration, a 1.8% increase during autumn migration, and a 0.9% increase during the summer. More impervious surface was associated with higher NMP species richness during the winter. A 10% increase in the proportion of impervious surface was associated with a 6.1-9.8% increase in NMP species richness. Higher ALAN was associated with lower NMP species richness during the winter and summer, and higher NMP species richness during spring and autumn migration. A 50% increase in ALAN was associated with a 3.0-3.6% decrease in NMP species richness during the winter, a 1.7% increase during spring migration, a 2.1% decrease during the summer, and a 5.0% increase during autumn migration. These findings highlight the variable effects of ALAN and urban landcover on the seasonal occurrence of NMP species in urban areas, the value of tree canopy cover during migration and the breeding season, and the importance of reducing ALAN during migration.
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Affiliation(s)
- Frank A La Sorte
- Cornell Lab of Ornithology, Cornell University, Ithaca, NY, 14850, USA.
| | - Kyle G Horton
- Colorado State University, Fish, Wildlife, and Conservation Biology, Fort Collins, CO, 80524, USA
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17
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Escarela G, Rodríguez CE, Núñez-Antonio G. Copula modeling of receiver operating characteristic and predictiveness curves. Stat Med 2020; 39:4252-4266. [PMID: 32929756 DOI: 10.1002/sim.8723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/13/2020] [Accepted: 07/18/2020] [Indexed: 12/13/2022]
Abstract
Receiver operating characteristic (ROC) and predictiveness curves are graphical tools to study the discriminative and predictive power of a continuous-valued marker in a binary outcome. In this paper, a copula-based construction of the joint density of the marker and the outcome is developed for plotting and analyzing both curves. The methodology only requires a copula function, the marginal distribution of the marker, and the prevalence rate for the model to be characterized. The adoption of the Gaussian copula and the customization of the margin for the marker are proposed for such characterization. The computation of both curves is numerically more feasible than methods that attempt to obtain one curve in terms of the other. Estimation is carried out using maximum likelihood and resampling-based methods. Randomized quantile residuals from each conditional distribution are employed for both assessing the adequacy of the model and identifying outliers. The performance of the estimators of both curves and their underlying quantities is evaluated in simulation studies that assume different dependence structures and sample sizes. The methods are illustrated with an analysis of the level of progesterone receptor gene expression for the diagnosis and prediction of estrogen receptor-positive breast cancer.
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Affiliation(s)
- Gabriel Escarela
- Department of Mathematics, Universidad Autónoma Metropolitana - Iztapalapa, Mexico City, Mexico
| | - Carlos Erwin Rodríguez
- Department of Probability and Statistics, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Gabriel Núñez-Antonio
- Department of Mathematics, Universidad Autónoma Metropolitana - Iztapalapa, Mexico City, Mexico
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Zhao Y, Gijbels I, Van Keilegom I. Inference for semiparametric Gaussian copula model adjusted for linear regression using residual ranks. BERNOULLI 2020. [DOI: 10.3150/20-bej1208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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19
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On the Relationship of Cryptocurrency Price with US Stock and Gold Price Using Copula Models. MATHEMATICS 2020. [DOI: 10.3390/math8111859] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This paper examines the relationship of the leading financial assets, Bitcoin, Gold, and S&P 500 with GARCH-Dynamic Conditional Correlation (DCC), Nonlinear Asymmetric GARCH DCC (NA-DCC), Gaussian copula-based GARCH-DCC (GC-DCC), and Gaussian copula-based Nonlinear Asymmetric-DCC (GCNA-DCC). Under the high volatility financial situation such as the COVID-19 pandemic occurrence, there exist a computation difficulty to use the traditional DCC method to the selected cryptocurrencies. To solve this limitation, GC-DCC and GCNA-DCC are applied to investigate the time-varying relationship among Bitcoin, Gold, and S&P 500. In terms of log-likelihood, we show that GC-DCC and GCNA-DCC are better models than DCC and NA-DCC to show relationship of Bitcoin with Gold and S&P 500. We also consider the relationships among time-varying conditional correlation with Bitcoin volatility, and S&P 500 volatility by a Gaussian Copula Marginal Regression (GCMR) model. The empirical findings show that S&P 500 and Gold price are statistically significant to Bitcoin in terms of log-return and volatility.
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20
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Han Z, De Oliveira V. Maximum likelihood estimation of Gaussian copula models for geostatistical count data. COMMUN STAT-SIMUL C 2020; 49:1957-1981. [DOI: 10.1080/03610918.2018.1508705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Zifei Han
- Vertex Pharmaceuticals, Boston, MA, USA
| | - Victor De Oliveira
- Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, TX, USA
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21
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Sustainable Causal Interpretation with Board Characteristics: Caveat Emptor. SUSTAINABILITY 2020. [DOI: 10.3390/su12083429] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The study of a causal interpretation of board and firm characteristics, that is, a hidden dependence relationship on the causal inference among board and firm characteristics, is an important but unaddressed issue in the corporate governance literature. Using diverse advanced statistical methods and focusing on Tobin’s Q, we find that (i) not all board variables previously found to be significant are “robust” to latent variable data analysis, and (ii) those variables that are consistently significant differ markedly in latent structural equation analysis. Our analyses provide researchers interested in board issues with an important caveat: Focusing on the dependence structure of available board variables affected by latent factors may introduce a new horizon in corporate finance.
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22
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Alqawba M, Diawara N. Copula-based Markov zero-inflated count time series models with application. J Appl Stat 2020; 48:786-803. [DOI: 10.1080/02664763.2020.1748581] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Mohammed Alqawba
- Department of Mathematics, College of Sciences and Arts, Qassim University, Al Rass, Saudi Arabia
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA, USA
| | - Norou Diawara
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA, USA
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Bevilacqua M, Caamaño‐Carrillo C, Arellano‐Valle RB, Morales‐Oñate V. Non‐Gaussian geostatistical modeling using (skew) t processes. Scand Stat Theory Appl 2020. [DOI: 10.1111/sjos.12447] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Moreno Bevilacqua
- Departamento de Estadística Universidad de Valparaíso
- Millennium Nucleus Center for the Discovery of Structures in Complex Data
| | | | | | - Víctor Morales‐Oñate
- Departamento de Desarrollo Ambiente y Territorio, Facultad Latinoamericana de Ciencias Sociales
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24
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Analysis of Tail Dependence between Sovereign Debt Distress and Bank Non-Performing Loans. SUSTAINABILITY 2020. [DOI: 10.3390/su12020747] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We investigate the tail dependence between sovereign debt distress and bank non-performing loans (NPLs) using a large sample of developed and emerging countries in recent decades. Considering the feedback loop of sovereign debt and bank loan distress, we use three copula models to analyze the asymmetry of tail dependence structure between sovereign debt exposure and bank NPLs. We use the Gaussian copula marginal regression to control the concurrent impact of other macroeconomic variables. We provide evidence that sovereign debt indicates an important determinant of NPLs. We also find that there is tail dependence between sovereign debt distress and bank NPLs, whereas the tail dependence coefficients vary across countries. Our findings shed light on the influence of fiscal distress on bank loan distress and provide immediate implications for the design of macro prudential and financial policy.
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Guo F, Ma W, Wang L. Semiparametric estimation of copula models with nonignorable missing data. J Nonparametr Stat 2019. [DOI: 10.1080/10485252.2019.1702660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Feng Guo
- School of Statistics and Data Science & LPMC, Nankai University, Tianjin, People's Republic of China
| | - Wei Ma
- School of Statistics and Data Science & LPMC, Nankai University, Tianjin, People's Republic of China
| | - Lei Wang
- School of Statistics and Data Science & LPMC, Nankai University, Tianjin, People's Republic of China
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French JT, Wang HH, Grant WE, Tomeček JM. Dynamics of animal joint space use: a novel application of a time series approach. MOVEMENT ECOLOGY 2019; 7:38. [PMID: 31867110 PMCID: PMC6902482 DOI: 10.1186/s40462-019-0183-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 11/26/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Animal use is a dynamic phenomenon, emerging from the movements of animals responding to a changing environment. Interactions between animals are reflected in patterns of joint space use, which are also dynamic. High frequency sampling associated with GPS telemetry provides detailed data that capture space use through time. However, common analyses treat joint space use as static over relatively long periods, masking potentially important changes. Furthermore, linking temporal variation in interactions to covariates remains cumbersome. We propose a novel method for analyzing the dynamics of joint space use that permits straightforward incorporation of covariates. This method builds upon tools commonly used by researchers, including kernel density estimators, utilization distribution intersection metrics, and extensions of linear models. METHODS We treat the intersection of the utilization distributions of two individuals as a time series. The series is linked to covariates using copula-based marginal beta regression, an alternative to generalized linear models. This approach accommodates temporal autocorrelation and the bounded nature of the response variable. Parameters are easily estimated with maximum likelihood and trend and error structures can be modeled separately. We demonstrate the approach by analyzing simulated data from two hypothetical individuals with known utilization distributions, as well as field data from two coyotes (Canis latrans) responding to appearance of a carrion resource in southern Texas. RESULTS Our analysis of simulated data indicated reasonably precise estimates of joint space use can be achieved with commonly used GPS sampling rates (s.e.=0.029 at 150 locations per interval). Our analysis of field data identified an increase in spatial interactions between the coyotes that persisted for the duration of the study, beyond the expected duration of the carrion resource. Our analysis also identified a period of increased spatial interactions before appearance of the resource, which would not have been identified by previous methods. CONCLUSIONS We present a new approach to the analysis of joint space use through time, building upon tools commonly used by ecologists, that permits a new level of detail in the analysis of animal interactions. The results are easily interpretable and account for the nuances of bounded serial data in an elegant way.
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Affiliation(s)
- Justin T. French
- Department of Wildlife & Fisheries Science, Texas A&M University, 534 John Kimbrough Blvd., College Station, 77843 USA
| | - Hsiao-Hsuan Wang
- Department of Wildlife & Fisheries Science, Texas A&M University, 534 John Kimbrough Blvd., College Station, 77843 USA
| | - William E. Grant
- Department of Wildlife & Fisheries Science, Texas A&M University, 534 John Kimbrough Blvd., College Station, 77843 USA
| | - John M. Tomeček
- Department of Wildlife & Fisheries Science, Texas A&M University, 534 John Kimbrough Blvd., College Station, 77843 USA
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Alqawba M, Diawara N, Rao Chaganty N. Zero-inflated count time series models using Gaussian copula. Seq Anal 2019. [DOI: 10.1080/07474946.2019.1648922] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Mohammed Alqawba
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, Virginia, USA
| | - Norou Diawara
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, Virginia, USA
| | - N. Rao Chaganty
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, Virginia, USA
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What Coins Lead in the Cryptocurrency Market: Using Copula and Neural Networks Models. JOURNAL OF RISK AND FINANCIAL MANAGEMENT 2019. [DOI: 10.3390/jrfm12030132] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Exploring dependence structures between financial time series has been important within a wide range of applications. The main aim of this paper is to examine dependence relationships among five well-known cryptocurrencies—Bitcoin, Ethereum, Litecoin, Ripple, and Stella—by a copula directional dependence (CDD). By employing a neural network autoregression model to avoid the serial dependence in each individual cryptocurrency, we generate residuals of the fitted models with time series of daily log-returns in percentage of the five cryptocurrencies and then we apply a Gaussian copula marginal beta regression model to the residuals to explore the CDD. The results show that the CDD from Bitcoin to Litecoin is highest among all ordered directional dependencies and the CDDs from Ethereum to the other four cryptocurrencies are relatively higher than the CDDs to Ethereum from those cryptocurrencies. This finding implies that the return shocks of Bitcoin have the most effect on Litecoin and the return shocks of Ethereum relatively influence the shocks on the other four cryptocurrencies instead of being affected by them. This allows investors to build the market-timing strategies by observing the directional flow of return shocks among cryptocurrencies.
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Meulman JJ, van der Kooij AJ, Duisters KLW. ROS Regression: Integrating Regularization with Optimal Scaling Regression. Stat Sci 2019. [DOI: 10.1214/19-sts697] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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30
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Alqawba M, Diawara N, Kim JM. Copula directional dependence of discrete time series marginals. COMMUN STAT-SIMUL C 2019. [DOI: 10.1080/03610918.2019.1630434] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Mohammed Alqawba
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, Virginia, USA
| | - Norou Diawara
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, Virginia, USA
| | - Jong-Min Kim
- Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris, Morris, Minnesota, USA
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31
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Estimation of a digitised Gaussian ARMA model by Monte Carlo Expectation Maximisation. Comput Stat Data Anal 2019. [DOI: 10.1016/j.csda.2018.10.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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32
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Directional dependence between major cities in China based on copula regression on air pollution measurements. PLoS One 2019; 14:e0213148. [PMID: 30870434 PMCID: PMC6417661 DOI: 10.1371/journal.pone.0213148] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 02/17/2019] [Indexed: 11/19/2022] Open
Abstract
Air pollution is well-known as a major risk to public health, causing various diseases including pulmonary and cardiovascular diseases. As social concern increases, the amount of air pollution data is increasing rapidly. The purpose of this study is to statistically characterize dependence between major cities in China based on a measure of directional dependence estimated from PM2.5 measurements. As a measure of the directional dependence, we propose the so-called copula directional dependence (CDD) using beta regression models. An advantage of the CDD is that it does not rely on strict assumptions of specific probability distributions or linearity. We used hourly PM2.5 measurement data collected at four major cities in China: Beijing, Chengdu, Guangzhou, and Shanghai, from 2013 to 2017. After accounting for autocorrelation in the PM2.5 time series via nonlinear autoregressive models, CDDs between the four cities were estimated to produce directed network structures of statistical dependence. In addition, a statistical method was proposed to test the directionality of dependence between each pair of cities. From the PM2.5 data, we could discover that Chengdu and Guangzhou are the most closely related cities and that the directionality between them has changed once during 2013 to 2017, which implies a major economic or environmental change in these Chinese regions.
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Petterle RR, Bonat WH, Scarpin CT. Quasi-beta Longitudinal Regression Model Applied to Water Quality Index Data. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2019. [DOI: 10.1007/s13253-019-00360-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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34
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A transition model for analyzing multivariate longitudinal data using Gaussian copula approach. ASTA-ADVANCES IN STATISTICAL ANALYSIS 2019. [DOI: 10.1007/s10182-018-00346-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Lee N, Kim JM. Copula directional dependence for inference and statistical analysis of whole-brain connectivity from fMRI data. Brain Behav 2019; 9:e01191. [PMID: 30592175 PMCID: PMC6346668 DOI: 10.1002/brb3.1191] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 11/22/2018] [Accepted: 11/29/2018] [Indexed: 11/20/2022] Open
Abstract
INTRODUCTION Inferring connectivity between brain regions has been raising a lot of attention in recent decades. Copula directional dependence (CDD) is a statistical measure of directed connectivity, which does not require strict assumptions on probability distributions and linearity. METHODS In this work, CDDs between pairs of local brain areas were estimated based on the fMRI responses of human participants watching a Pixar animation movie. A directed connectivity map of fourteen predefined local areas was obtained for each participant, where the network structure was determined by the strengths of the CDDs. A resampling technique was further applied to determine the statistical significance of the connectivity directions in the networks. In order to demonstrate the effectiveness of the suggested method using CDDs, statistical group analysis was conducted based on graph theoretic measures of the inferred directed networks and CDD intensities. When the 129 fMRI participants were grouped by their age (3-5 year-old, 7-12 year-old, adult) and gender (F, M), nonparametric two-way analysis of variance (ANOVA) results could identify which cortical regions and connectivity structures correlated with the two physiological factors. RESULTS Especially, we could identify that (a) graph centrality measures of the frontal eye fields (FEF), the inferior temporal gyrus (ITG), and the temporopolar area (TP) were significantly affected by aging, (b) CDD intensities between FEF and the primary motor cortex (M1) and between ITG and TP were highly significantly affected by aging, and (c) CDDs between M1 and the anterior prefrontal cortex (aPFC) were highly significantly affected by gender. SOFTWARE The R source code for fMRI data preprocessing, estimation of directional dependences, network visualization, and statistical analyses are available at https://github.com/namgillee/CDDforFMRI.
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Affiliation(s)
- Namgil Lee
- Department of Information Statistics, Kangwon National University, Chuncheon, South Korea
| | - Jong-Min Kim
- Statistics Discipline, Division of Sciences and Mathematics, University of Minnesota-Morris, Morris, Minnesota
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36
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He Y, Zhang X, Zhang L. Variable selection for high dimensional Gaussian copula regression model: An adaptive hypothesis testing procedure. Comput Stat Data Anal 2018. [DOI: 10.1016/j.csda.2018.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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37
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Kürüm E, Jeske DR, Behrendt CE, Lee P. A copula model for joint modeling of longitudinal and time‐invariant mixed outcomes. Stat Med 2018; 37:3931-3943. [DOI: 10.1002/sim.7855] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2017] [Revised: 04/27/2018] [Accepted: 06/01/2018] [Indexed: 01/07/2023]
Affiliation(s)
- Esra Kürüm
- Department of Statistics University of California Riverside CA
| | - Daniel R. Jeske
- Department of Statistics University of California Riverside CA
| | - Carolyn E. Behrendt
- Division of Biostatistics, Department of Information Sciences Beckman Research Institute of the City of Hope Duarte CA
| | - Peter Lee
- Department of Immuno‐Oncology City of Hope Comprehensive Cancer Center Duarte CA
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38
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Popovic GC, Hui FK, Warton DI. A general algorithm for covariance modeling of discrete data. J MULTIVARIATE ANAL 2018. [DOI: 10.1016/j.jmva.2017.12.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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39
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Modelling the Covariance Structure in Marginal Multivariate Count Models: Hunting in Bioko Island. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2017. [DOI: 10.1007/s13253-017-0284-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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41
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Jiryaie F, Withanage N, Wu B, de Leon A. Gaussian copula distributions for mixed data, with application in discrimination. J STAT COMPUT SIM 2015. [DOI: 10.1080/00949655.2015.1077386] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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43
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Comorbidity of chronic diseases in the elderly: Patterns identified by a copula design for mixed responses. Comput Stat Data Anal 2015. [DOI: 10.1016/j.csda.2015.02.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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44
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Struchen R, Reist M, Zinsstag J, Vial F. Investigating the potential of reported cattle mortality data in Switzerland for syndromic surveillance. Prev Vet Med 2015; 121:1-7. [PMID: 26032722 DOI: 10.1016/j.prevetmed.2015.04.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Revised: 04/05/2015] [Accepted: 04/19/2015] [Indexed: 11/16/2022]
Abstract
Systems for the identification and registration of cattle have gradually been receiving attention for use in syndromic surveillance, a relatively recent approach for the early detection of infectious disease outbreaks. Real or near real-time monitoring of deaths or stillbirths reported to these systems offer an opportunity to detect temporal or spatial clusters of increased mortality that could be caused by an infectious disease epidemic. In Switzerland, such data are recorded in the "Tierverkehrsdatenbank" (TVD). To investigate the potential of the Swiss TVD for syndromic surveillance, 3 years of data (2009-2011) were assessed in terms of data quality, including timeliness of reporting and completeness of geographic data. Two time-series consisting of reported on-farm deaths and stillbirths were retrospectively analysed to define and quantify the temporal patterns that result from non-health related factors. Geographic data were almost always present in the TVD data; often at different spatial scales. On-farm deaths were reported to the database by farmers in a timely fashion; stillbirths were less timely. Timeliness and geographic coverage are two important features of disease surveillance systems, highlighting the suitability of the TVD for use in a syndromic surveillance system. Both time series exhibited different temporal patterns that were associated with non-health related factors. To avoid false positive signals, these patterns need to be removed from the data or accounted for in some way before applying aberration detection algorithms in real-time. Evaluating mortality data reported to systems for the identification and registration of cattle is of value for comparing national data systems and as a first step towards a European-wide early detection system for emerging and re-emerging cattle diseases.
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Affiliation(s)
- Rahel Struchen
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Schwarzenburgstrasse 155, 3003 Bern, Switzerland.
| | - Martin Reist
- Swiss Federal Food Safety and Veterinary Office, Schwarzenburgstrasse 155, 3003 Bern, Switzerland
| | - Jakob Zinsstag
- Swiss Tropical and Public Health Institute, University of Basel, Socinstrasse 57, 4051 Basel, Switzerland
| | - Flavie Vial
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Schwarzenburgstrasse 155, 3003 Bern, Switzerland
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Ganjali M, Baghfalaki T. A Copula Approach to Joint Modeling of Longitudinal Measurements and Survival Times Using Monte Carlo Expectation-Maximization with Application to AIDS Studies. J Biopharm Stat 2014; 25:1077-99. [PMID: 25372017 DOI: 10.1080/10543406.2014.971584] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Joint modeling of longitudinal measurements and time to event data is often performed by fitting a shared parameter model. Another method for joint modeling that may be used is a marginal model. As a marginal model, we use a Gaussian model for joint modeling of longitudinal measurements and time to event data. We consider a regression model for longitudinal data modeling and a Weibull proportional hazard model for event time data modeling. A Gaussian copula is used to consider the association between these two models. A Monte Carlo expectation-maximization approach is used for parameter estimation. Some simulation studies are conducted in order to illustrate the proposed method. Also, the proposed method is used for analyzing a clinical trial dataset.
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Affiliation(s)
- M Ganjali
- a Department of Statistics , Shahid Beheshti University , Tehran , Iran
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46
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Segers J, van den Akker R, Werker BJM. Semiparametric Gaussian copula models: Geometry and efficient rank-based estimation. Ann Stat 2014. [DOI: 10.1214/14-aos1244] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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47
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Bai Y, Kang J, Song PXK. Efficient pairwise composite likelihood estimation for spatial-clustered data. Biometrics 2014; 70:661-70. [PMID: 24945876 DOI: 10.1111/biom.12199] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2012] [Revised: 04/01/2014] [Accepted: 04/01/2014] [Indexed: 11/28/2022]
Abstract
Spatial-clustered data refer to high-dimensional correlated measurements collected from units or subjects that are spatially clustered. Such data arise frequently from studies in social and health sciences. We propose a unified modeling framework, termed as GeoCopula, to characterize both large-scale variation, and small-scale variation for various data types, including continuous data, binary data, and count data as special cases. To overcome challenges in the estimation and inference for the model parameters, we propose an efficient composite likelihood approach in that the estimation efficiency is resulted from a construction of over-identified joint composite estimating equations. Consequently, the statistical theory for the proposed estimation is developed by extending the classical theory of the generalized method of moments. A clear advantage of the proposed estimation method is the computation feasibility. We conduct several simulation studies to assess the performance of the proposed models and estimation methods for both Gaussian and binary spatial-clustered data. Results show a clear improvement on estimation efficiency over the conventional composite likelihood method. An illustrative data example is included to motivate and demonstrate the proposed method.
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Affiliation(s)
- Yun Bai
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A
| | - Jian Kang
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, U.S.A
| | - Peter X-K Song
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A
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Guolo A, Varin C. Beta regression for time series analysis of bounded data, with application to Canada Google® Flu Trends. Ann Appl Stat 2014. [DOI: 10.1214/13-aoas684] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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49
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Wu B, de Leon AR. Gaussian Copula Mixed Models for Clustered Mixed Outcomes, With Application in Developmental Toxicology. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2013. [DOI: 10.1007/s13253-013-0155-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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