Beta-Binomial Kriging: A New Approach to Modeling Spatially Correlated Proportions

Authors

Aimee Schwab, University of Nebraska-LincolnFollow

Date of this Version

8-2015

Citation

Schwab, A. D. (2015). Beta-Binomial Kriging: A New Approach for Modeling Spatially Correlated Proportions (Unpublished doctoral dissertation). University of Nebraska-Lincoln, Lincoln, NE.

Comments

A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Statistics, Under the Supervision of Professor David B. Marx. Lincoln, Nebraska: August, 2015

Copyright (c) 2015 Aimee D. Schwab

Abstract

Spatially correlated count data sets appear often in applied data analysis problems, but there is little consensus in the literature about how best to analyze the data. The two prevailing approaches provide accurate parameter estimates and predictions, at the cost of model interpretability and simplicity. This dissertation will present a new approach to modeling spatially correlated binomial observations: beta-binomial kriging. The model proposed here is a modified form of spatial kriging which assumes the data are generated from a correlated beta-binomial distribution. Given this assumption, the spatial parameters and predicted values can be estimated using simple matrix algebra. Beta-binomial kriging will be thoroughly assessed in the dissertation and shown to be a competitive option for modeling spatially correlated proportions. The model’s advantages will be illustrated using childhood vaccination rates.

Adviser: David Marx