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PARTIAL LEAST SQUARES ON DATA WITH MISSING COVARIATES: A COMPARISON APPROACH

Tudorascu, Dana Larisa (2009) PARTIAL LEAST SQUARES ON DATA WITH MISSING COVARIATES: A COMPARISON APPROACH. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

The correlation between any two random variables can be estimated using a variety of techniques including parametric methods based on the Pearson correlation coefficient, nonparametric methods, and regression analysis. While these estimators have been widely used, the computation of these estimates in the presence of missing data has not been as widely studied. There has been some work addressing the estimation of parameters in the presence of missing data for regression analysis; including imputation, inverse probability weighted regression and weighted estimating equations. However, there has been little work focused on the estimation of the correlation coefficient. To assess the usefulness of these methods in a practical setting, we present simulation studies comparing imputation, inverse probability weighting and complete cases and provide recommendations on the basis of these results. Furthermore, computation of Partial Least Squares (PLS) scores with the correlation matrix computed using the above mentioned techniques are also presented. We apply these results in a positron emission tomography data set consisting of several different brain regions as response variables and cognitive tasks as covariates of interest. Alzheimer's disease is a progressive and fatal health disease. The application presented in this work is significant for public health since it provides us with a better understanding of variability in different brain regions as it relates to neuropsychological tests that are helpful in diagnosis of progressive brain disease (i.e Alzheimer's disease).


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Tudorascu, Dana Larisadanatud@gmail.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWeissfeld, Lisa A.lweis@pitt.eduLWEIS
Committee MemberPrice, Julie C.pricejc@upmc.du
Committee MemberKong, Lanlkong@pitt.eduLKONG
Committee MemberAnderson, Stewart J.sja@pitt.eduSJA
Date: 28 September 2009
Date Type: Completion
Defense Date: 4 June 2009
Approval Date: 28 September 2009
Submission Date: 10 June 2009
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: COVARIATES; MISSING DATA; PARTIAL LEAST SQUARES
Other ID: http://etd.library.pitt.edu/ETD/available/etd-06102009-145729/, etd-06102009-145729
Date Deposited: 10 Nov 2011 19:46
Last Modified: 15 Nov 2016 13:44
URI: http://d-scholarship.pitt.edu/id/eprint/8068

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