Back to results

Virginia Tech

Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data

Abstract

dc:description.abstract

Ecological data is often fraught with many problems such as Missing Data and Spatial Correlation. In this dissertation we use a data set collected by the Ohio EPA as motivation for studying techniques to address these problems. The data set is concerned with the benthic health of Ohio's waterways. A new method for incorporating covariate structure and missing data mechanisms into missing data analysis is considered. This method allows us to detect relationships other popular methods do not allow. We then further extend this method into model selection. In the special case where the unobserved covariates are assumed normally distributed we use the Bayesian Model Averaging method to average the models, select the highest probability model and do variable assessment. Accuracy in calculating the posterior model probabilities using the Laplace approximation and an approximation based on the Bayesian Information Criterion (BIC) are explored. It is shown that the Laplace approximation is superior to the BIC based approximation using simulation. Finally, Hierarchical Spatial Linear Models are considered for the data and we show how to combine analysis which have spatial correlation within and between clusters.

Degree

thesis:*
Name thesis:degree_name
Ph. D.
Level thesis:degree_level
doctoral
Discipline thesis:degree_discipline
Statistics
Department dc:contributor.department
Statistics
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2003

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Boone, Edward L.
Chair dc:contributor.committeechair
  • Ye, Keying
Committee members dc:contributor.committeemember
  • Anderson-Cook, Christine M.
  • Terrell, George R.
  • Prins, Samantha C. Bates
  • Smith, Eric P.

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
etd-02072003-143850
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/26141

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Boone, Edward L.. Bayesian Methodology for Missing Data, Model Selection and Hierarchical Spatial Models with Application to Ecological Data. doctoral thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/26141