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Variable selection for binary spatial regression: Penalized quasi‐likelihood approach

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  • Wenning Feng
  • Abdhi Sarkar
  • Chae Young Lim
  • Tapabrata Maiti

Abstract

We consider the problem of selecting covariates in a spatial regression model when the response is binary. Penalized likelihood‐based approach is proved to be effective for both variable selection and estimation simultaneously. In the context of a spatially dependent binary variable, an uniquely interpretable likelihood is not available, rather a quasi‐likelihood might be more suitable. We develop a penalized quasi‐likelihood with spatial dependence for simultaneous variable selection and parameter estimation along with an efficient computational algorithm. The theoretical properties including asymptotic normality and consistency are studied under increasing domain asymptotics framework. An extensive simulation study is conducted to validate the methodology. Real data examples are provided for illustration and applicability. Although theoretical justification has not been made, we also investigate empirical performance of the proposed penalized quasi‐likelihood approach for spatial count data to explore suitability of this method to a general exponential family of distributions.

Suggested Citation

  • Wenning Feng & Abdhi Sarkar & Chae Young Lim & Tapabrata Maiti, 2016. "Variable selection for binary spatial regression: Penalized quasi‐likelihood approach," Biometrics, The International Biometric Society, vol. 72(4), pages 1164-1172, December.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1164-1172
    DOI: 10.1111/biom.12525
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    References listed on IDEAS

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    Cited by:

    1. Zhong, Yan & Sang, Huiyan & Cook, Scott J. & Kellstedt, Paul M., 2023. "Sparse spatially clustered coefficient model via adaptive regularization," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

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