Bias reduction and model selection in misspecified models

Hidenori Okumura

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 8, 2751-2765

Abstract: This article concerns maximum penalized likelihood estimation in misspecified generalized linear models with independent and identically distributed observations. A new method for simultaneous model selection and estimation with bias reduction is proposed in the framework. A discontinuous penalized likelihood function is used, and an approximate method to solve the discontinuous optimization problem is introduced. The proposed method has model selection consistency in a sparse regression setting in which the dimension of predictors is fixed and the sample size increases. The efficiency of the proposed method is illustrated through a finite simulation study.

Date: 2023
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DOI: 10.1080/03610926.2021.1959613

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