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Composite likelihood estimation for models of spatial ordinal data and spatial proportional data with zero/one values
Author(s) -
Feng Xiaoping,
Zhu Jun,
Lin PeiSheng,
SteenAdams Michelle M.
Publication year - 2014
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2306
Subject(s) - tobit model , likelihood function , mathematics , statistics , spatial analysis , ordinal data , inference , statistical inference , ordinal regression , computer science , algorithm , estimation theory , artificial intelligence
In this paper, we consider a spatial ordered probit model for analyzing spatial ordinal data with two or more ordered categories and, further, a spatial Tobit model for spatial proportional data with zero/one values. We develop a composite likelihood approach for parameter estimation and inference, which aims to balance statistical efficiency and computational efficiency for large datasets. The parameter estimates are obtained by maximizing a composite likelihood function via a quasi‐Newton algorithm. The asymptotic properties of the maximum composite likelihood estimates are established under suitable regularity conditions. An estimate of the inverse of the Godambe information matrix is used for computing the standard errors, and the computation is further expedited by parallel computing. A simulation study is conducted to evaluate the performance of the proposed methods, followed by a real ecological data example. The connections between the spatial ordered probit model and the spatial Tobit model are explored using both simulated and real data. Copyright © 2014 John Wiley & Sons, Ltd.