Efficient pairwise composite likelihood estimation for spatial-clustered data.
Reads0
Chats0
TLDR
This work proposes an efficient composite likelihood approach in that the estimation efficiency is resulted from a construction of over‐identified joint composite estimating equations, and the statistical theory for the proposed estimation is developed by extending the classical theory of the generalized method of moments.Abstract:
Spatial-clustered data refer to high-dimensional correlated measurements collected from units or subjects that are spatially clustered. Such data arise frequently from studies in social and health sciences. We propose a unified modeling framework, termed as GeoCopula, to characterize both large-scale variation, and small-scale variation for various data types, including continuous data, binary data, and count data as special cases. To overcome challenges in the estimation and inference for the model parameters, we propose an efficient composite likelihood approach in that the estimation efficiency is resulted from a construction of over-identified joint composite estimating equations. Consequently, the statistical theory for the proposed estimation is developed by extending the classical theory of the generalized method of moments. A clear advantage of the proposed estimation method is the computation feasibility. We conduct several simulation studies to assess the performance of the proposed models and estimation methods for both Gaussian and binary spatial-clustered data. Results show a clear improvement on estimation efficiency over the conventional composite likelihood method. An illustrative data example is included to motivate and demonstrate the proposed method.read more
Citations
More filters
Journal ArticleDOI
Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95
Journal ArticleDOI
Non-Gaussian geostatistical modeling using (skew) t processes
Moreno Bevilacqua,Christian Caamaño-Carrillo,Reinaldo B. Arellano-Valle,Víctor Morales-Oñate +3 more
TL;DR: A new model for regression and dependence analysis when addressing spatial data with possibly heavy tails and an asymmetric marginal distribution is proposed and the use of the weighted pairwise likelihood as a method of estimation for the t process is investigated.
Journal ArticleDOI
Spatial cluster detection of regression coefficients in a mixed‐effects model
TL;DR: A mixed‐effects model for spatial cluster detection that takes spatial correlation into account is proposed, and the introduced random effects explain extra variability among the spatial responses beyond the cluster effect, thus reducing the false positive rate.
Posted Content
Adaptive estimating function inference for non-stationary determinantal point processes
TL;DR: In this paper, the truncation distance is adapted automatically to the point process being fitted and where the approach integrates seamlessly with the asymptotic framework for non-stationary determinantal point processes.
Journal ArticleDOI
Adaptive estimating function inference for non-stationary determinantal point processes
TL;DR: This article establishes asymptotic normality of estimating function estimators in a very general setting of nonstationary point processes and adapts this result to the case of non stationary determinantal point processes, which are an important class of models for repulsive point patterns.
References
More filters
Journal ArticleDOI
Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
Journal ArticleDOI
Large sample properties of generalized method of moments estimators
Book
The jackknife, the bootstrap, and other resampling plans
TL;DR: The Delta Method and the Influence Function Cross-Validation, Jackknife and Bootstrap Balanced Repeated Replication (half-sampling) Random Subsampling Nonparametric Confidence Intervals as mentioned in this paper.
Journal ArticleDOI
The Jackknife: The Bootstrap and Other Resampling Plans.
Leone Y. Low,Bradley Efron +1 more
Related Papers (5)
Copula-based geostatistical modeling of continuous and discrete data including covariates
Hannes Kazianka,Jürgen Pilz +1 more