Graphical tools for model selection in generalized linear models

2013 ◽  
Vol 32 (25) ◽  
pp. 4438-4451 ◽  
Author(s):  
K. Murray ◽  
S. Heritier ◽  
S. Müller
Keyword(s):  
Model Selection ◽  
Generalized Linear Models ◽  
Linear Models

Large-scale model selection in misspecified generalized linear models

Biometrika ◽  
2021 ◽  
Author(s):  
Emre Demirkaya ◽  
Yang Feng ◽  
Pallavi Basu ◽  
Jinchi Lv
Keyword(s):  
Model Selection ◽  
Generalized Linear Models ◽  
Large Scale ◽  
Linear Models ◽  
Information Criterion ◽  
Scale Model ◽  
High Dimensional ◽  
Model Selection Consistency ◽  
New Information ◽  
Large Scale Model

Summary Model selection is crucial both to high-dimensional learning and to inference for contemporary big data applications in pinpointing the best set of covariates among a sequence of candidate interpretable models. Most existing work assumes implicitly that the models are correctly specified or have fixed dimensionality, yet both are prevalent in practice. In this paper, we exploit the framework of model selection principles under the misspecified generalized linear models presented in Lv and Liu (2014) and investigate the asymptotic expansion of the posterior model probability in the setting of high-dimensional misspecified models.With a natural choice of prior probabilities that encourages interpretability and incorporates the Kullback–Leibler divergence, we suggest the high-dimensional generalized Bayesian information criterion with prior probability for large-scale model selection with misspecification. Our new information criterion characterizes the impacts of both model misspecification and high dimensionality on model selection. We further establish the consistency of covariance contrast matrix estimation and the model selection consistency of the new information criterion in ultra-high dimensions under some mild regularity conditions. The numerical studies demonstrate that our new method enjoys improved model selection consistency compared to its main competitors.

scholarly journals Blockwise AICc for Model Selection in Generalized Linear Models

2017 ◽  
Vol 22 (6) ◽  
pp. 523-533 ◽  
Author(s):  
Guofeng Song ◽  
Xiaogang Dong ◽  
Jiafeng Wu ◽  
You-Gan Wang
Keyword(s):  
Model Selection ◽  
Generalized Linear Models ◽  
Linear Models
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