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Variable selection for joint mean and dispersion models of the inverse Gaussian distribution

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  • Liucang Wu
  • Huiqiong Li

Abstract

The choice of distribution is often made on the basis of how well the data appear to be fitted by the distribution. The inverse Gaussian distribution is one of the basic models for describing positively skewed data which arise in a variety of applications. In this paper, the problem of interest is simultaneously parameter estimation and variable selection for joint mean and dispersion models of the inverse Gaussian distribution. We propose a unified procedure which can simultaneously select significant variables in mean and dispersion model. With appropriate selection of the tuning parameters, we establish the consistency of this procedure and the oracle property of the regularized estimators. Simulation studies and a real example are used to illustrate the proposed methodologies. Copyright Springer-Verlag 2012

Suggested Citation

  • Liucang Wu & Huiqiong Li, 2012. "Variable selection for joint mean and dispersion models of the inverse Gaussian distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(6), pages 795-808, August.
    • Handle: RePEc:spr:metrik:v:75:y:2012:i:6:p:795-808
    DOI: 10.1007/s00184-011-0352-x
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    References listed on IDEAS

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    Cited by:

    1. Xu, Dengke & Zhang, Zhongzhan, 2013. "A semiparametric Bayesian approach to joint mean and variance models," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1624-1631.
    2. Yeşim Güney & Yetkin Tuaç & Şenay Özdemir & Olcay Arslan, 2021. "Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution," Computational Statistics, Springer, vol. 36(2), pages 805-827, June.

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