Masters Theses
Abstract
"A generalization of a prediction interval procedure for the binomial distribution to the case of the binomial distribution with dependent trials is considered. Several different methods have been developed for obtaining prediction intervals for the binomial distribution. An unpublished study by Vlieger and Samaranayake has shown that two of these methods achieve coverage probabilities close to nominal levels. The proposed method is an extension of one of these methods and is based on the maximum likelihood predictive density proposed by Lejeune and Faulkenberry. A simulation study was carried out to investigate the coverage probabilities of the proposed prediction bounds.
This method requires the availability of a closed form expression for the maximum likelihood parameter estimates. For the binomial distribution with dependent trials, three estimators asymptotically equivalent to the maximum likelihood estimators (MLE) due to Klotz, Price, and Kim and Bai are available. The use of these estimators, in place of the MLE, is considered because the MLEs cannot be expressed in a closed form. Simulation results show the prediction bounds based on Klotz’s estimator exhibit reasonable coverage probabilities in the case of the binomial distribution with dependent trials"--Abstract, page iii.
Advisor(s)
Samaranayake, V. A.
Committee Member(s)
Gadbury, Gary L.
Bryant, Richard Ralph
Department(s)
Mathematics and Statistics
Degree Name
M.S. in Mathematics
Publisher
University of Missouri--Rolla
Publication Date
Spring 2003
Pagination
viii, 82 pages
Note about bibliography
Includes bibliographical references (page 81).
Rights
© 2003 Florian Sebastian Rueck, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Subject Headings
Binomial distribution
Prediction (Logic)
Thesis Number
T 8240
Print OCLC #
53238349
Link to Catalog Record
Recommended Citation
Rueck, Florian Sebastian, "Prediction intervals for the binomial distribution with dependent trials" (2003). Masters Theses. 2335.
https://scholarsmine.mst.edu/masters_theses/2335