Award Date

1-1-2005

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematical Sciences

First Committee Member

Ashok Singh

Number of Pages

38

Abstract

This paper is concerned with the Bayesian approach to estimate the mean when encountered with left-censored data sets. Considering the joint non-informative prior, we derived the posterior probability density function of the mean of left-censored data. However, this density function is not recognizable and we can not analytically integrate it to obtain the normalizing constant. In other words, we can not compute analytically the posterior pdf or posterior moments. Numerical integration involving the adaptive Simpson quadrature rule was used in Mat-lab to obtain the posterior mean and the upper credible limit (UCL). Several numerical examples are given which illustrate the practical application of these results.

Keywords

Bayesian; Detects; Estimation; Mean; Normal; Presence

Controlled Subject

Mathematics

File Format

pdf

File Size

839.68 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Permissions

If you are the rightful copyright holder of this dissertation or thesis and wish to have the full text removed from Digital Scholarship@UNLV, please submit a request to digitalscholarship@unlv.edu and include clear identification of the work, preferably with URL.

Rights

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

Download


COinS