ORCID ID

0000-0001-5902-9738

Date Awarded

2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.)

Department

Applied Science

Advisor

Lawrence M. Leemis

Committee Member

Christopher A. Del Negro

Committee Member

Daniel Runfola

Committee Member

Heather Sasinowska

Abstract

This dissertation has three principle areas of research: mixed type random variables, confidence regions, and golden quantile rank sets. While each offers a specific focus, some common themes persist; broadly stated, there are three. First, computational graphics play a critical role. Second, software development facilitates implementation and accessibility. Third, statistical analysis---often attributable to the aforementioned automation---provides valuable insights and applications. Each of the principle research areas are briefly summarized next. Mixed type random variables are a hybrid of continuous and discrete random variables, having components of both continuous probability density and discrete probability mass. This dissertation illustrates the challenges inherent in plotting mixed type distributions, and introduces an algorithm that addresses those issues. It considers sums and products of mixed type random variables, and supports its conclusions using Monte Carlo simulation experiments. Lastly, it introduces MixedAPPL, a computer algebra system software package designed for manipulating mixed type random variables. Confidence regions are a multi-dimensional version of a confidence interval. They are helpful to visualize and quantify uncertainty surrounding a point estimate. We begin by developing efficient plot algorithms for two-dimensional confidence regions. This research focuses specifically on likelihood-ratio based confidence regions for two-parameter univariate probability models, although the plot techniques are transferable to any two-dimensional setting. The R package 'conf' is introduced, which automates these confidence region plot algorithms for complete and right-censored data sets. Among its benefits, 'conf' provides access to Monte Carlo simulation experiments for confidence region coverage to an extent not possible previously. The corresponding coverage analysis results include reference tables for the Weibull, normal, and log-logistic distributions. These reference tables yield confidence region plots with exact coverage. The final topic is the introduction and analysis of a golden quantile rank set (GQRS). The term quantile rank set is used here to denote the population cumulative distribution function values corresponding to a sample. A GQRS can be thought of as "perfectly" representative of their population distribution because samples corresponding to a GQRS result in an estimator(s) matching the associated true population parameter(s). This unique characteristic is not applicable for all estimators and/or distributions, but when present, provides valuable insights and applications. Specifically, applications include an alternative (and at times computationally superior) method for parameter estimation and an exact actual coverage methodology for confidence regions (at times in which currently only estimates exist). Distributions with a GQRS associated with maximum likelihood estimation include the normal, exponential, Weibull, log logistic, and one-parameter exponential power distributions.

DOI

http://dx.doi.org/10.21220/s2-yer7-0964

Rights

© The Author

Available for download on Monday, June 07, 2021

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