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On Estimation and Selection of Autologistic Regression Models via Penalized Pseudolikelihood

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An Erratum to this article was published on 04 May 2017

Abstract

Autologistic regression models are suitable for relating spatial binary responses in ecology to covariates such as environmental factors. For big ecological data, pseudolikelihood estimation is appealing due to its ease of computation, but at least two challenges remain. Although an important issue, it is unclear how model selection may be carried out under pseudolikelihood. In addition, for assessing the variation of pseudolikelihood estimates, parametric bootstrap using Monte Carlo simulation is often used but may be infeasible for very large data sizes. Here both these issues are addressed by developing a penalized pseudolikelihood estimation method and an approximation of the variance of the parameter estimates. A simulation study is conducted to evaluate the performance of the proposed method, followed by a data example in a study of land cover in relation to land ownership characteristics. Extension of these models and methods to spatial-temporal binary data is further discussed. This article has supplementary material online.

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Author information

Authors and Affiliations

  1. Department of Statistics, University of Wisconsin, Madison, WI, 53706, USA

    Rao Fu & Andrew L. Thurman

  2. Department of Statistics and Department of Entomology, University of Wisconsin, Madison, WI, 53706, USA

    Jun Zhu

  3. School of Statistics, Renmin University, Beijing, 100872, China

    Tingjin Chu

  4. Department of Environmental Studies, University of New England, Biddeford, ME, 04005, USA

    Michelle M. Steen-Adams

Authors
  1. Rao Fu
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  2. Andrew L. Thurman
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  3. Tingjin Chu
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  4. Michelle M. Steen-Adams
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  5. Jun Zhu
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Corresponding author

Correspondence to Jun Zhu.

Additional information

An erratum to this article is available at http://dx.doi.org/10.1007/s13253-017-0281-x.

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Fu, R., Thurman, A.L., Chu, T. et al. On Estimation and Selection of Autologistic Regression Models via Penalized Pseudolikelihood. JABES 18, 429–449 (2013). https://doi.org/10.1007/s13253-013-0144-z

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  • DOI: https://doi.org/10.1007/s13253-013-0144-z

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