Abstract/Details

Analysis of data in presence of censored observations


2010 2010

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Abstract (summary)

In this dissertation, the problems of computing confidence interval, tolerance interval and prediction interval based on the samples with non-detectable values (i.e., type I censored samples) from normal and related distributions are addressed. Firstly two types of imputation approach have been investigated: one based on the maximum likelihood estimates (MLEs) of the parameters, and the second uses some ad hoc estimates that are particularly suitable for sample sizes that are small or moderately large. Secondly we have investigated the inferential problems concerning the arithmetic mean and the quantiles of a lognormal distribution based on censored samples. Here we have used procedures based on generalized generalized variable approach and modified signed log-likelihood ratio test (MSLRT) statistics. In our investigation we have compared the performance of these two procedures along with that of the signed log-likelihood ratio test (SLRT) statistic for inference concerning the mean and quantiles of a lognormal distribution. Monte Carlo simulation is used to investigate the performance of our procedures. For each of the problems considered, the results are illustrated using practical examples.

Indexing (details)


Subject
Statistics
Classification
0463: Statistics
Identifier / keyword
Pure sciences; Censored data; Data analysis; Detection limits; Likelihood-based approach; Tolerance limits
Title
Analysis of data in presence of censored observations
Author
Mallick, Avishek
Number of pages
76
Publication year
2010
Degree date
2010
School code
1363
Source
DAI-B 71/06, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9781124028705
Advisor
Krishnamoorthy, Kalimuthu
University/institution
University of Louisiana at Lafayette
University location
United States -- Louisiana
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3410666
ProQuest document ID
506372104
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/506372104
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