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Bayesian and maximin optimal designs for heteroscedastic multi-factor regression models. Stat Pap (Berl) 2022. [DOI: 10.1007/s00362-022-01368-y] [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]
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Dette H, Liu X, Yue RX. Design admissibility and de la Garza phenomenon in multifactor experiments. Ann Stat 2022. [DOI: 10.1214/21-aos2147] [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)
- Holger Dette
- Fakultät für Mathematik, Ruhr-Universität Bochum
| | - Xin Liu
- College of Science, Donghua University
| | - Rong-Xian Yue
- Department of Mathematics, Shanghai Normal University
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Attributional & Consequential Life Cycle Assessment: Definitions, Conceptual Characteristics and Modelling Restrictions. SUSTAINABILITY 2021. [DOI: 10.3390/su13137386] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
To assess the potential environmental impact of human/industrial systems, life cycle assessment (LCA) is a very common method. There are two prominent types of LCA, namely attributional (ALCA) and consequential (CLCA). A lot of literature covers these approaches, but a general consensus on what they represent and an overview of all their differences seems lacking, nor has every prominent feature been fully explored. The two main objectives of this article are: (1) to argue for and select definitions for each concept and (2) specify all conceptual characteristics (including translation into modelling restrictions), re-evaluating and going beyond findings in the state of the art. For the first objective, mainly because the validity of interpretation of a term is also a matter of consensus, we argue the selection of definitions present in the 2011 UNEP-SETAC report. ALCA attributes a share of the potential environmental impact of the world to a product life cycle, while CLCA assesses the environmental consequences of a decision (e.g., increase of product demand). Regarding the second objective, the product system in ALCA constitutes all processes that are linked by physical, energy flows or services. Because of the requirement of additivity for ALCA, a double-counting check needs to be executed, modelling is restricted (e.g., guaranteed through linearity) and partitioning of multifunctional processes is systematically needed (for evaluation per single product). The latter matters also hold in a similar manner for the impact assessment, which is commonly overlooked. CLCA, is completely consequential and there is no limitation regarding what a modelling framework should entail, with the coverage of co-products through substitution being just one approach and not the only one (e.g., additional consumption is possible). Both ALCA and CLCA can be considered over any time span (past, present & future) and either using a reference environment or different scenarios. Furthermore, both ALCA and CLCA could be specific for average or marginal (small) products or decisions, and further datasets. These findings also hold for life cycle sustainability assessment.
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He L. Bayesian optimal designs for multi-factor nonlinear models. STAT METHOD APPL-GER 2021. [DOI: 10.1007/s10260-020-00522-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Li R, Zhang Y. Two-stage estimation and simultaneous confidence band in partially nonlinear additive model. METRIKA 2021. [DOI: 10.1007/s00184-021-00808-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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He L. Optimal designs for multi-factor nonlinear models based on the second-order least squares estimator. Stat Probab Lett 2018. [DOI: 10.1016/j.spl.2018.01.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Kim Y, Jang DH, Yi S. The Maximin Robust Design for the Uncertainty of Parameters of Michaelis-Menten Model. KOREAN JOURNAL OF APPLIED STATISTICS 2014. [DOI: 10.5351/kjas.2014.27.7.1269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Zhu L, Dasgupta T, Huang Q. A D-Optimal Design for Estimation of Parameters of an Exponential-Linear Growth Curve of Nanostructures. Technometrics 2014. [DOI: 10.1080/00401706.2013.866600] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Rodríguez C, Ortiz I, Martínez I. Locally and maximin optimal designs for multi-factor nonlinear models. STATISTICS-ABINGDON 2014. [DOI: 10.1080/02331888.2014.922562] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Abebe HT, Tan FES, Van Breukelen GJP, Serroyen J, Berger MPF. On the Choice of a Prior for Bayesian D-Optimal Designs for the Logistic Regression Model with a Single Predictor. COMMUN STAT-SIMUL C 2014. [DOI: 10.1080/03610918.2012.745556] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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