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Maximally selected test statistics
Descriptive
TitleInfo
Title
Maximally selected test statistics
SubTitle
methodology and application
Name
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Ma
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Yuhui
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1972-
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Yuhui Ma
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author
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Strickland
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Pamela A. Ohman
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Pamela A. Ohman Strickland
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Advisory Committee
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chair
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Lu
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Shou-En
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Shou-En Lu
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Advisory Committee
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internal member
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Shih
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Weichung Joe
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Weichung Joe Shih
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Advisory Committee
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internal member
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Fiedler
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Nancy
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Nancy Fiedler
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Advisory Committee
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internal member
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Xie
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Min-ge
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Min-ge Xie
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Advisory Committee
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outside member
Name
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Rutgers University
Role
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degree grantor
Name
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Edward J. Bloustein School of Planning and Pub. Policy
Role
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school
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Text
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theses
OriginInfo
DateCreated
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2012
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(qualifier = exact);
(type = degree)
2012-01
CopyrightDate
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2000
Place
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xx
Language
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eng
Abstract
In clinical or public health research studies, an investigator often assumes that some continuous predictive variable X allows classifying study population into a risk and a normal group with respect to a response variable Y. The aim of these research efforts is to transform a continuous variable into a binary variable by identifying a threshold or cutpoint in the predictor to distinguish different groups with high or low probabilities of favorable outcomes. Several methods including maximally selected chi-square statistics, maximally selected rank statistics and Koziol’s exact finite sample distribution approach to search for the optimal cut point have been reviewed and compared in Chapter 1. Since utilizing the maximally selected rank statistic to analyze semi-continuous predictors has not been discussed in the literatures, this dissertation provides the comparison of the null distribution, power curve, precision of cut point estimation between semi-continuous and continuous predictive variables via simulation. In Chapter 2, we confirmed the critical values to reject the null hypotheses are lower in semi-continuous predictors compare to continuous predictors. In Chapter 3, we show the power of maximally selected rank statistic from the semi-continuous predictor is stochastically larger than that from the continuous predictor. In Chapter 4, we found besides the sample size and effect size, the location of the true cut-point also affects the precision of the cut-point estimates. Compared to the continuous predictor, the semi-continuous predictor has higher percentage of correct cut-point estimates. The null distributions for semi-continuous predictor simulated in Chapter 1 are then applied to the study of “lead exposure, HPA dysfunction, blood pressure and hypertension risk” (Fiedler 2010) in Chapter 6 to determine the cut point in blood lead level that triggers increased stress. This application focused on the multivariate relationship between predictor variables and response variable, which were not discussed in the literature. After adjusted by other confounder variables through the regression residuals, a significant cut-point of 2 μg/dL in blood lead level is identified. Since the use of the regression residual of the response variable violates the independence assumption of this maximally selected rank statistics, this dissertation also demonstrated the robustness of this assumption in chapter 5.
Subject
(authority = RUETD)
Topic
Biostatistics
Subject
(authority = ETD-LCSH)
Topic
Statistics--Research
Subject
(authority = ETD-LCSH)
Topic
Mathematical statistics--Research
Subject
(authority = ETD-LCSH)
Topic
Sampling (Statistics)--Research
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Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
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Title
Bloustein Grad. School of Planning and Pub. Policy ETD Collection
Identifier
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rucore10003400001
Identifier
ETD_3756
Identifier
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http://hdl.rutgers.edu/1782.1/rucore10003400001.ETD.000064223
PhysicalDescription
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electronic resource
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application/pdf
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text/xml
Extent
x, 97 p. : ill.
Note
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Dr.P.H.
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Includes bibliographical references
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by Yuhui Ma
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NjNbRU
Identifier
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doi:10.7282/T3W9587M
Genre
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ETD doctoral
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Ma
GivenName
Yuhui
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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2011-12-27 11:58:25
AssociatedEntity
Name
Yuhui Ma
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Copyright holder
Affiliation
Rutgers University. Edward J. Bloustein School of Planning and Pub. Policy
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License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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