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Efficient pairwise composite likelihood estimation for spatial-clustered data.

Yun Bai1, Jian Kang2, Peter X-K Song1

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, U.S.A.

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|June 20, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces GeoCopula, a novel framework for analyzing spatial-clustered data common in health and social sciences. The proposed composite likelihood method offers improved estimation efficiency and computational feasibility for various data types.

Keywords:
Gaussian copulaGeneralized method of momentsGeographical clusterMatérn classRegression

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Area of Science:

  • Statistics
  • Spatial Analysis
  • Biostatistics

Background:

  • Spatial-clustered data, characterized by high-dimensional correlated measurements from spatially grouped units, are prevalent in social and health sciences.
  • Existing methods may face challenges in characterizing both large-scale and small-scale variations within such data.
  • Handling diverse data types like continuous, binary, and count data requires flexible modeling approaches.

Purpose of the Study:

  • To propose a unified modeling framework, GeoCopula, for spatial-clustered data.
  • To develop an efficient composite likelihood approach for parameter estimation and inference.
  • To address the computational challenges associated with analyzing complex spatial data structures.

Main Methods:

  • Development of the GeoCopula framework to model large-scale and small-scale variations.
  • Application of an efficient composite likelihood approach using over-identified joint composite estimating equations.
  • Extension of generalized method of moments theory for statistical inference.

Main Results:

  • Simulation studies demonstrated improved estimation efficiency compared to conventional composite likelihood methods for Gaussian and binary spatial-clustered data.
  • The proposed GeoCopula model effectively characterizes various data types.
  • The composite likelihood approach proved computationally feasible.

Conclusions:

  • GeoCopula provides a unified and efficient framework for spatial-clustered data analysis.
  • The proposed composite likelihood method enhances estimation efficiency and computational feasibility.
  • This approach offers a valuable tool for researchers in social and health sciences dealing with complex spatial data.