Award Date

1-1-2005

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematical Sciences

First Committee Member

Ashok K. Singh

Number of Pages

44

Abstract

The Kaplan-Meier (K-M) estimator is a non-parametric estimator of the survival function, used in lifetesting and medical follow-up studies where some of the observations are incomplete (right-censored data). In environmental applications, the user is faced with the problem of contaminant concentration falling below the limit of detection (DL) of the instrument (left-censored data). The K-M estimator has recently been proposed in environmental literature for computing the Upper Confidence Limit (UCL) of the mean in the presence of nondetects in environmental data sets. The properties of this UCL, however, have not been investigated. In this thesis, I propose to use Monte Carlo simulation to study the performance of the K-M method for computing the UCL of the mean.

Keywords

Confidence; Environmental; Investigation; Kaplan; Limit; Mean; Meier; Nondetects; Population; Samples; Upper

Controlled Subject

Statistics

File Format

pdf

File Size

1372.16 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.

Identifier

https://doi.org/10.25669/icec-7cyl


Share

COinS