Abstract

The paper considers statistical inference for R = P(X < Y) in the case when both X and Y have generalized gamma distributions. The maximum likelihood estimators for R are developed in the case when either all three parameters of the generalized gamma distributions are unknown or when the shape parameters are known. In addition, objective Bayes estimators based on non informative priors are constructed when the shape parameters are known. Finally, the uniform minimum variance unbiased estimators (UMVUE) are derived in the case when only the scale parameters are unknown.

Thesis Completion

2007

Semester

Spring

Advisor

Pensky, Marianna

Degree

Bachelor of Science (B.S.)

College

College of Sciences

Degree Program

Mathematics

Subjects

Dissertations, Academic -- Sciences; Sciences -- Dissertations, Academic

Format

PDF

Identifier

DP0020693

Language

English

Rights

Written permission granted by copyright holder to the University of Central Florida Libraries to digitize and distribute for nonprofit, educational purposes.

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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Included in

Mathematics Commons

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