Semester of Graduation
First Major Professor
Master of Science (MS)
The class of Markov random field models known as auto-models provides a flexible and highly-interpretable model structure for analysis of lattice data, which may be of particular interest for research in agriculture, forestry, image construction, or genetics. This paper proposes a Markov random field model with a bivariate Binary-Gaussian response, which provides a foundation for reliable statistical inference with multivariate response distributions of mixed types in the presence of spatial dependence. Pseudo-likelihood-based estimation and inference are investigated via simulation study. The model adequately captures both covariate and spatial dependence relationships under a not-too-strong spatial dependence regime, however the definition of ``strong spatial dependence" is a difficult question when distributions are mixed. The model is implemented in 2 cases: data from wheat field trials and data on observations of prairie chickens. The full bivariate model outperforms univariate and non-spatial models in capturing spatial dependence and covariate relationships together.
Harms, Steven, "A centered bivariate Markov random field model for mixed-response lattice data" (2019). Creative Components. 189.