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Variable selection for varying dispersion beta regression model

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Listed:
  • Weihua Zhao
  • Riquan Zhang
  • Yazhao Lv
  • Jicai Liu

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.

Suggested Citation

  • Weihua Zhao & Riquan Zhang & Yazhao Lv & Jicai Liu, 2014. "Variable selection for varying dispersion beta regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 95-108, January.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:1:p:95-108
    DOI: 10.1080/02664763.2013.830284
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    References listed on IDEAS

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    Cited by:

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    3. Zhao, Weihua & Lian, Heng & Zhang, Riquan & Lai, Peng, 2016. "Estimation and variable selection for proportional response data with partially linear single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 40-56.
    4. Amon, Julian & Hornik, Kurt, 2022. "Is it all bafflegab? – Linguistic and meta characteristics of research articles in prestigious economics journals," Journal of Informetrics, Elsevier, vol. 16(2).

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