Efficient pairwise composite likelihood estimation for spatial-clustered data

DOI10.1111/biom.12199zbMath1299.62048OpenAlexW2169308707WikidataQ30832232 ScholiaQ30832232MaRDI QIDQ2927619

Peter X.-K. Song, Jian Kang, Yun Bai

Publication date: 4 November 2014

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4431962

zbMATH Keywords

generalized method of momentsregressionGaussian copulaMatérn classgeographical cluster

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Computational methods for problems pertaining to biology (92-08)

Related Items (12)

Limitations and performance of three approaches to Bayesian inference for Gaussian copula regression models of discrete data ⋮ Knowledge Learning of Insurance Risks Using Dependence Models ⋮ Maximum likelihood estimation of Gaussian copula models for geostatistical count data ⋮ Composite pseudo-likelihood estimation for pair-tractable copulas such as Archimedean, Archimax and related hierarchical extensions ⋮ Seasonal count time series ⋮ A class of random fields with two-piece marginal distributions for modeling point-referenced data with spatial outliers ⋮ Modelling Point Referenced Spatial Count Data: A Poisson Process Approach ⋮ Copula and composite quantile regression-based estimating equations for longitudinal data ⋮ Robust and efficient estimating equations for longitudinal data partial linear models and its applications ⋮ Unnamed Item ⋮ Partial ML estimation for spatial autoregressive nonlinear probit models with autoregressive disturbances ⋮ Models for Geostatistical Binary Data: Properties and Connections

Uses Software

• geoRglm
• gcmr

Cites Work

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