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EnglishModel selection can be defined as the task of estimating the performance of different models in order to choose the most parsimonious one, among a potentially very large set of candidate statistical models. We propose a graphical representation to be considered as an extension to the class of mixed models of the deviance plot proposed in the literature within the framework of classical and generalized linear models. This graphical representation allows, once a reduced number of models have been selected, to identify important covariates focusing only on the fixed effects component, assuming the random part properly specified. Nevertheless, we suggest also a standalone figure representing the residual random variance ratio: a cross-evaluation of the two graphical representations will allow to derive some conclusions on the random part specification of the model and a more accurate selection of the final model.
Sciandra, M., & Plaia, A. (2018). A graphical model selection tool for mixed models. COMMUNICATIONS IN STATISTICS. SIMULATION AND COMPUTATION, 47(9), 2624-2638 [10.1080/03610918.2017.1353617].
| Data di pubblicazione: | 2018 | |
| Titolo: | A graphical model selection tool for mixed models | |
| Autori: | ||
| Citazione: | Sciandra, M., & Plaia, A. (2018). A graphical model selection tool for mixed models. COMMUNICATIONS IN STATISTICS. SIMULATION AND COMPUTATION, 47(9), 2624-2638 [10.1080/03610918.2017.1353617]. | |
| Rivista: | ||
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1080/03610918.2017.1353617 | |
| Abstract: | Model selection can be defined as the task of estimating the performance of different models in order to choose the most parsimonious one, among a potentially very large set of candidate statistical models. We propose a graphical representation to be considered as an extension to the class of mixed models of the deviance plot proposed in the literature within the framework of classical and generalized linear models. This graphical representation allows, once a reduced number of models have been selected, to identify important covariates focusing only on the fixed effects component, assuming the random part properly specified. Nevertheless, we suggest also a standalone figure representing the residual random variance ratio: a cross-evaluation of the two graphical representations will allow to derive some conclusions on the random part specification of the model and a more accurate selection of the final model. | |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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http://hdl.handle.net/10447/334648
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