Advisor

Schaffer, Jay Ryan, 1969-

Advisor

Lalonde, Trent L.

Committee Member

Franklin, Scott B.

Committee Member

Shafie, Khalil

Department

Applied Statistics and Research Methods

Institution

University of Northern Colorado

Type of Resources

Text

Place of Publication

Greeley (Colo.)

Publisher

University of Northern Colorado

Date Created

5-1-2014

Genre

Thesis

Extent

175 pages

Digital Origin

Born digital

Description

Binary data that are correlated across space and time often occur in health and ecological studies. The centered spatial-temporal autologistic regression model (Wang & Zheng, 2013) accounts for the spatial and temporal dependence that can occur in binary data. Statistical inference for the autologistic model has been based upon pseudolikelihood, Monte Carlo maximum likelihood, Monte Carlo expectation maximization or Bayesian hierarchical models. However, these methods require the full conditional distribution to be defined and with the complexity of spatial and temporal dependence and interactions between observations can cause convergence problems and increase computation time. An alternative approach to likelihood based methods for spatial-temporal data is to use generalized method of moments, a method not currently used for spatial-temporal binary data. Two different generalized method of moments approaches based on a set of moment conditions is constructed with respect to spatial neighborhoods to account for the spatial and temporal dependence of the data. Comparisons of estimation methods using a small simulation and two data sets show that the generalized method of moments approaches perform well in specific data situations.

Degree type

PhD

Degree Name

Doctoral

Language

English

Local Identifiers

Kaufeld_unco_0161D_10314

Rights Statement

Copyright is held by author.

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