Analyzing spatially distributed binary data using independent-block estimating equations

Samuel D. Oman*, Victoria Landsman, Yohay Carmel, Ronen Kadmon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

We estimate the relation between binary responses and corresponding covariate vectors, both observed over a large spatial lattice. We assume a hierarchical generalized linear model with probit link function, partition the lattice into blocks, and adopt the working assumption of independence between the blocks to obtain an easily solved estimating equation. Standard errors are obtained using the "sandwich" estimator together with window subsampling (Sherman, 1996, Journal of the Royal Statistical Society, Series B 58, 509-523). We apply this to a large data set describing long-term vegetation growth, together with two other approximate-likelihood approaches: pairwise composite likelihood (CL) and estimation under a working assumption of independence. The independence and CL methods give similar point estimates and standard errors, while the independent-block approach gives considerably smaller standard errors, as well as more easily interpretable point estimates. We present numerical evidence suggesting this increased efficiency may hold more generally.

Original languageAmerican English
Pages (from-to)892-900
Number of pages9
JournalBiometrics
Volume63
Issue number3
DOIs
StatePublished - Sep 2007

Keywords

  • Binary variables
  • Composite likelihood
  • Generalized estimating equations
  • Probit regression
  • Spatial dependence

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