Award Date

5-1-2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Mathematical Sciences

First Committee Member

Hokwon Cho

Second Committee Member

Malwane Ananda

Third Committee Member

Kaushik Ghosh

Fourth Committee Member

Amei Amei

Fifth Committee Member

Jaewon Lim

Number of Pages

124

Abstract

The subject of Statistical Classification is concerned with identifying and allocating future observations into one of the pre-categorized classes based on the characteristics of the objects. Typically, these decisions to classify and categorize the objects have been dependent on identifying a system of classification, and from there, determining attributes for sorting.

In past decades, from discriminant analysis, various methods have been developed for classification. In particular, the rise of artificial intelligence (AI), machine learning, and statistical learning theory has made it possible to consider improving the existing methods along with new developments and more comprehensive schemes in conjunction with data-driven methods.

In this dissertation, we propose innovation using multiple decision-theoretic perspectives, such as the Indifference-Zone (IZ) approach and the Subset Selection (SS) method, to improve and clarify how these classification decisions can be made.

Keywords

Indifference-Zone Approach; Multiple Decision Problem; Preferable Predictor Vector; Selection and Ranking Methodologies; Statistical Classification; Statistical Learning

Disciplines

Statistics and Probability

File Format

pdf

File Size

1409 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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