Variable selection for varying dispersion beta regression model |
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Authors: | Weihua Zhao Yazhao Lv Jicai Liu |
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Affiliation: | 1. School of Science, NanTong University, NanTong 226007, People's Republic of China;2. School of Finance and Statistics, East China Normal University, Shanghai 200241, People's Republic of China |
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Abstract: | The beta regression models are commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). In this paper, we consider the issue of variable selection for beta regression models with varying dispersion (VBRM), in which both the mean and the dispersion depend upon predictor variables. Based on a penalized likelihood method, the consistency and the oracle property of the penalized estimators are established. Following the coordinate descent algorithm idea of generalized linear models, we develop new variable selection procedure for the VBRM, which can efficiently simultaneously estimate and select important variables in both mean model and dispersion model. Simulation studies and body fat data analysis are presented to illustrate the proposed methods. |
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Keywords: | beta regression varying dispersion variable selection coordinate descent algorithm BIC |
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