Analyzing spatially distributed binary data using independent-block estimating equations.
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 B58, 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.
Oman
Samuel D
SD
Department of Statistics, Hebrew University of Jerusalem, Mount Scopus 91905 Jerusalem, Israel. oman@mscc.huji.ac.il
Landsman
Victoria
V
Carmel
Yohay
Y
Kadmon
Ronen
R
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016013:Likelihood Functions; D016015:Logistic Models; D008954:Models, Biological; D015233:Models, Statistical; D012044:Regression Analysis; D016008:Statistical Distributions