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Carter JB, Browning CR, Boettner B, Pinchak N, Calder CA. LAND-USE FILTERING FOR NONSTATIONARY SPATIAL PREDICTION OF COLLECTIVE EFFICACY IN AN URBAN ENVIRONMENT. Ann Appl Stat 2024; 18:794-818. [PMID: 38831930 PMCID: PMC11146085 DOI: 10.1214/23-aoas1813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Collective efficacy-the capacity of communities to exert social control toward the realization of their shared goals-is a foundational concept in the urban sociology and neighborhood effects literature. Traditionally, empirical studies of collective efficacy use large sample surveys to estimate collective efficacy of different neighborhoods within an urban setting. Such studies have demonstrated an association between collective efficacy and local variation in community violence, educational achievement, and health. Unlike traditional collective efficacy measurement strategies, the Adolescent Health and Development in Context (AHDC) Study implemented a new approach, obtaining spatially-referenced, place-based ratings of collective efficacy from a representative sample of individuals residing in Columbus, OH. In this paper we introduce a novel nonstationary spatial model for interpolation of the AHDC collective efficacy ratings across the study area, which leverages administrative data on land use. Our constructive model specification strategy involves dimension expansion of a latent spatial process and the use of a filter defined by the land-use partition of the study region to connect the latent multivariate spatial process to the observed ordinal ratings of collective efficacy. Careful consideration is given to the issues of parameter identifiability, computational efficiency of an MCMC algorithm for model fitting, and fine-scale spatial prediction of collective efficacy.
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Affiliation(s)
- J. Brandon Carter
- Department of Statistics and Data Sciences, University of Texas at Austin
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2
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Dean CB, El‐Shaarawi AH, Esterby SR, Mills Flemming J, Routledge RD, Taylor SW, Woolford DG, Zidek JV, Zwiers FW. Canadian contributions to environmetrics. CAN J STAT 2022. [DOI: 10.1002/cjs.11743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Charmaine B. Dean
- Department of Statistics and Actuarial Science University of Waterloo 200 University Avenue West, Waterloo Ontario Canada N2L 3G1
| | - Abdel H. El‐Shaarawi
- Department of Statistics, Faculty of Economics and Political Science Cairo University Cairo Egypt
| | - Sylvia R. Esterby
- Department of Computer Science Mathematics, Physics and Statistics, University of British Columbia, Okanagan Campus 3187, University Way, Kelowna British Columbia Canada V1V 1V7
| | - Joanna Mills Flemming
- Department of Mathematics and Statistics Dalhousie University 6316 Coburg Road ‐ PO BOX 15000, Halifax Nova Scotia Canada B3H 4R2
| | - Richard D. Routledge
- Department of Statistics and Actuarial Science Simon Fraser University 8888 University Drive, Burnaby British Columbia Canada V5A 1S6
| | - Stephen W. Taylor
- Pacific Forestry Centre 506 Burnside Road West, Victoria British Columbia Canada V8Z 1M5
| | - Douglas G. Woolford
- Department of Statistical & Actuarial Sciences The University of Western Ontario 1151 Richmond Street, London Ontario Canada N6A 5B7
| | - James V. Zidek
- Department of Statistics University of British Columbia 2207 Main Mall, Vancouver British Columbia Canada V6T 1Z4
| | - Francis W. Zwiers
- Pacific Climate Impacts Consortium (PCIC) University of Victoria University House 1, PO Box 1700 Stn CSC, Victoria British Columbia Canada V8W 2Y2
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3
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Salvaña MLO, Lenzi A, Genton MG. Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2078330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Mary Lai O. Salvaña
- Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Amanda Lenzi
- Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Marc G. Genton
- Statistics Program, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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4
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Sun B, Wu Y. Estimation of the Covariance Matrix in Hierarchical Bayesian Spatio-Temporal Modeling via Dimension Expansion. ENTROPY 2022; 24:e24040492. [PMID: 35455155 PMCID: PMC9024874 DOI: 10.3390/e24040492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/18/2022] [Accepted: 03/18/2022] [Indexed: 11/23/2022]
Abstract
Ozone concentrations are key indicators of air quality. Modeling ozone concentrations is challenging because they change both spatially and temporally with complicated structures. Missing data bring even more difficulties. One of our interests in this paper is to model ozone concentrations in a region in the presence of missing data. We propose a method without any assumptions on the correlation structure to estimate the covariance matrix through a dimension expansion method for modeling the semivariograms in nonstationary fields based on the estimations from the hierarchical Bayesian spatio-temporal modeling technique (Le and Zidek). Further, we apply an entropy criterion (Jin et al.) based on a predictive model to decide if new stations need to be added. This entropy criterion helps to solve the environmental network design problem. For demonstration, we apply the method to the ozone concentrations at 25 stations in the Pittsburgh region studied. The comparison of the proposed method and the one is provided through leave-one-out cross-validation, which shows that the proposed method is more general and applicable.
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Sauer A, Gramacy RB, Higdon D. Active Learning for Deep Gaussian Process Surrogates. Technometrics 2021. [DOI: 10.1080/00401706.2021.2008505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Affiliation(s)
- Andrew Zammit-Mangion
- School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, Australia
| | - Tin Lok James Ng
- School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, Australia
| | - Quan Vu
- School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW, Australia
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Qadir GA, Sun Y, Kurtek S. Estimation of Spatial Deformation for Nonstationary Processes via Variogram Alignment. Technometrics 2021. [DOI: 10.1080/00401706.2021.1883481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Ghulam A. Qadir
- CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Ying Sun
- CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Sebastian Kurtek
- Department of Statistics, The Ohio State University, Columbus, OH
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Chevalier C, Martius O, Ginsbourger D. Modeling Nonstationary Extreme Dependence With Stationary Max-Stable Processes and Multidimensional Scaling. J Comput Graph Stat 2020. [DOI: 10.1080/10618600.2020.1844213] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Clément Chevalier
- Institute of Statistics, University of Neuchâtel, Switzerland
- Oeschger Centre for Climate Change Research, University of Bern, Switzerland
| | - Olivia Martius
- Oeschger Centre for Climate Change Research, University of Bern, Switzerland
- Institute of Geography, University of Bern, Switzerland
| | - David Ginsbourger
- Oeschger Centre for Climate Change Research, University of Bern, Switzerland
- Institute of Mathematical Statistics and Actuarial Science, University of Bern, Switzerland
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9
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Kirsner D, Sansó B. Multi-scale shotgun stochastic search for large spatial datasets. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2020.106931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Chen L, Zhu H, Wang X. Modeling Spatiotemporal Distribution of Mosquitoes Abundance With Unobservable Environmental Factors. JOURNAL OF MEDICAL ENTOMOLOGY 2019; 56:65-71. [PMID: 30339250 PMCID: PMC6324192 DOI: 10.1093/jme/tjy118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Indexed: 05/31/2023]
Abstract
Mosquito trap counts are heavily influenced by environmental factors such as temperature and precipitation. However, some important geographic factors, such as land use and elevation of a particular site, are often either not recorded or simplify not observable. This is a major issue in building a predictive model for the mosquito trap counts over time across a particular region. The collective impact of all unobservable factors for one particular site is estimated by a hidden dimension method. Application to mosquito trap counts in Peel Region has shown that our model can significantly improve the modeling accuracy of the generalized linear model. This method may provide a significantly better characterization of the spatiotemporal distribution of mosquito (Diptera: Culicidae) abundance in areas with green lands or open spaces.
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Affiliation(s)
- Longbin Chen
- LAMPS and Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Huaiping Zhu
- LAMPS and Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Xiaogang Wang
- LAMPS and Department of Mathematics and Statistics, York University, Toronto, ON, Canada
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12
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Bayesian nonstationary Gaussian process models via treed process convolutions. ADV DATA ANAL CLASSI 2018. [DOI: 10.1007/s11634-018-0341-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Johnson LR, Gramacy RB, Cohen J, Mordecai E, Murdock C, Rohr J, Ryan SJ, Stewart-Ibarra AM, Weikel D. PHENOMENOLOGICAL FORECASTING OF DISEASE INCIDENCE USING HETEROSKEDASTIC GAUSSIAN PROCESSES: A DENGUE CASE STUDY. Ann Appl Stat 2018; 12:27-66. [PMID: 38623158 PMCID: PMC11017302 DOI: 10.1214/17-aoas1090] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
In 2015 the US federal government sponsored a dengue forecasting competition using historical case data from Iquitos, Peru and San Juan, Puerto Rico. Competitors were evaluated on several aspects of out-of-sample forecasts including the targets of peak week, peak incidence during that week, and total season incidence across each of several seasons. our team was one of the winners of that competition, outperforming other teams in multiple targets/locales. In this paper we report on our methodology, a large component of which, surprisingly, ignores the known biology of epidemics at large-for example, relationships between dengue transmission and environmental factors-and instead relies on flexible nonparametric nonlinear Gaussian process (GP) regression fits that "memorize" the trajectories of past seasons, and then "match" the dynamics of the unfolding season to past ones in real-time. Our phenomenological approach has advantages in situations where disease dynamics are less well understood, or where measurements and forecasts of ancillary covariates like precipitation are unavailable, and/or where the strength of association with cases are as yet unknown. In particular, we show that the GP approach generally outperforms a more classical generalized linear (autoregressive) model (GLM) that we developed to utilize abundant covariate information. We illustrate variations of our method(s) on the two benchmark locales alongside a full summary of results submitted by other contest competitors.
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15
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Cunha M, Gamerman D, Fuentes M, Paez M. A non-stationary spatial model for temperature interpolation applied to the state of Rio de Janeiro. J R Stat Soc Ser C Appl Stat 2017. [DOI: 10.1111/rssc.12207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
| | | | | | - Marina Paez
- Universidade Federal do Rio de Janeiro; Brazil
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16
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Maruotti A, Bulla J, Lagona F, Picone M, Martella F. Dynamic mixtures of factor analyzers to characterize multivariate air pollutant exposures. Ann Appl Stat 2017. [DOI: 10.1214/17-aoas1049] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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17
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Pratola MT, Harari O, Bingham D, Flowers GE. Design and Analysis of Experiments on Nonconvex Regions. Technometrics 2017. [DOI: 10.1080/00401706.2016.1164558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
| | - Ofir Harari
- Department of Statistics & Actuarial Sciences, Simon Fraser University, Burnaby, Canada
| | - Derek Bingham
- Department of Statistics & Actuarial Sciences, Simon Fraser University, Burnaby, Canada
| | - Gwenn E. Flowers
- Department of Earth Sciences, Simon Fraser University, Burnaby, Canada
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Pratola MT, Harari O, Bingham D, Flowers GE. Design and Analysis of Experiments on Nonconvex Regions. Technometrics 2017. [DOI: 10.1080/00401706.2015.1115674] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
| | - Ofir Harari
- Department of Statistics & Actuarial Sciences, Simon Fraser University Burnaby, V5A 1S6, B.C., Canada
| | - Derek Bingham
- Department of Statistics & Actuarial Sciences, Simon Fraser University Burnaby, V5A 1S6, B.C., Canada
| | - Gwenn E. Flowers
- Department of Earth Sciences, Simon Fraser University Burnaby, V5A 1S6, B.C., Canada
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Shand L, Li B. Modeling nonstationarity in space and time. Biometrics 2017; 73:759-768. [PMID: 28134977 DOI: 10.1111/biom.12656] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Revised: 12/01/2016] [Accepted: 12/01/2016] [Indexed: 11/28/2022]
Abstract
We propose to model a spatio-temporal random field that has nonstationary covariance structure in both space and time domains by applying the concept of the dimension expansion method in Bornn et al. (2012). Simulations are conducted for both separable and nonseparable space-time covariance models, and the model is also illustrated with a streamflow dataset. Both simulation and data analyses show that modeling nonstationarity in both space and time can improve the predictive performance over stationary covariance models or models that are nonstationary in space but stationary in time.
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Affiliation(s)
- Lyndsay Shand
- Department of Statistics, University of Illinois at Urbana-Champaign, Illinois, U.S.A
| | - Bo Li
- Department of Statistics, University of Illinois at Urbana-Champaign, Illinois, U.S.A
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A Fused Lasso Approach to Nonstationary Spatial Covariance Estimation. JOURNAL OF AGRICULTURAL, BIOLOGICAL, AND ENVIRONMENTAL STATISTICS 2016. [DOI: 10.1007/s13253-016-0251-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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21
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Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health. STATISTICS IN BIOSCIENCES 2016; 9:559-581. [PMID: 29225714 PMCID: PMC5711999 DOI: 10.1007/s12561-016-9150-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Accepted: 05/20/2016] [Indexed: 11/11/2022]
Abstract
Performing studies on the risks of environmental hazards on human health requires accurate estimates of exposures that might be experienced by the populations at risk. Often there will be missing data and in many epidemiological studies, the locations and times of exposure measurements and health data do not match. To a large extent this will be due to the health and exposure data having arisen from completely different data sources and not as the result of a carefully designed study, leading to problems of both ‘change of support’ and ‘misaligned data’. In such cases, a direct comparison of the exposure and health outcome is often not possible without an underlying model to align the two in the spatial and temporal domains. The Bayesian approach provides the natural framework for such models; however, the large amounts of data that can arise from environmental networks means that inference using Markov Chain Monte Carlo might not be computationally feasible in this setting. Here we adapt the integrated nested Laplace approximation to implement spatio–temporal exposure models. We also propose methods for the integration of large-scale exposure models and health analyses. It is important that any model structure allows the correct propagation of uncertainty from the predictions of the exposure model through to the estimates of risk and associated confidence intervals. The methods are demonstrated using a case study of the levels of black smoke in the UK, measured over several decades, and respiratory mortality.
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Gilani O, Berrocal VJ, Batterman SA. Non-stationary spatio-temporal modeling of traffic-related pollutants in near-road environments. Spat Spatiotemporal Epidemiol 2016; 18:24-37. [PMID: 27494957 DOI: 10.1016/j.sste.2016.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2015] [Revised: 03/05/2016] [Accepted: 03/24/2016] [Indexed: 11/27/2022]
Abstract
A problem often encountered in environmental epidemiological studies assessing the health effects associated with ambient exposure to air pollution is the spatial misalignment between monitors' locations and subjects' actual residential locations. Several strategies have been adopted to circumvent this problem and estimate pollutants concentrations at unsampled sites, including spatial statistical or geostatistical models that rely on the assumption of stationarity to model the spatial dependence in pollution levels. Although computationally convenient, the assumption of stationarity is often untenable for pollutants concentration, particularly in the near-road environment. Building upon the work of Fuentes (2001) and Schmidt et al. (2011), in this paper we present a non-stationary spatio-temporal model for three traffic-related pollutants in a localized near-road environment. Modeling each pollutant separately and independently, we express each pollutant's concentration as a mixture of two independent spatial processes, each equipped with a non-stationary covariance function with covariates driving the non-stationarity and the mixture weights.
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Affiliation(s)
- Owais Gilani
- Department of Biostatistics, University of Michigan, School of Public Health, Ann Arbor, MI 48109, United States; Department of Environmental Health Sciences, University of Michigan, School of Public Health, Ann Arbor, MI 48109, United States
| | - Veronica J Berrocal
- Department of Biostatistics, University of Michigan, School of Public Health, Ann Arbor, MI 48109, United States.
| | - Stuart A Batterman
- Department of Environmental Health Sciences, University of Michigan, School of Public Health, Ann Arbor, MI 48109, United States
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Huser R, Genton MG. Non-Stationary Dependence Structures for Spatial Extremes. JOURNAL OF AGRICULTURAL, BIOLOGICAL, AND ENVIRONMENTAL STATISTICS 2016. [DOI: 10.1007/s13253-016-0247-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Gramacy RB, Bingham D, Holloway JP, Grosskopf MJ, Kuranz CC, Rutter E, Trantham M, Drake RP. Calibrating a large computer experiment simulating radiative shock hydrodynamics. Ann Appl Stat 2015. [DOI: 10.1214/15-aoas850] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Choi I, Li B, Wang X. Nonparametric Estimation of Spatial and Space-Time Covariance Function. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2013. [DOI: 10.1007/s13253-013-0152-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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