Paper Info
Title
Maximum likelihood estimation of Gaussian copula models for geostatistical count data
Abstract
This work investigates the computation of maximum likelihood estimators in Gaussian copula models for geostatistical count data. This is a computationally challenging task because the likelihood function is only expressible as a high dimensional multivariate normal integral. Two previously proposed Monte Carlo methods are reviewed, the Genz-Bretz and Geweke-Hajivassiliou-Keane simulators, and a new method is investigated. The new method is based on the so-calleddata cloningalgorithm, which uses Markov chain Monte Carlo algorithms to approximate maximum likelihood estimators and their (asymptotic) variances in models with computationally challenging likelihoods. A simulation study is carried out to compare the statistical and computational efficiencies of the three methods. It is found that the three methods have similar statistical properties, but the Geweke-Hajivassiliou-Keane simulator requires the least computational effort. Hence, this is the method we recommend. A data analysis of Lansing Woods tree counts is used to illustrate the methods.
Year
DOI
Venue
2020
10.1080/03610918.2018.1508705
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Keywords
DocType
Volume
Data cloning,Gaussian random field,Markov chain Monte Carlo,Multivariate normal integral,Simulated likelihood
Journal
49.0
Issue
ISSN
Citations 
8.0
0361-0918
0
PageRank 
References 
Authors
0.34
4
2
Name
Order
Citations
PageRank
Zifei Han100.34
Victor De Oliveira211.83