MULTIFACTOR DIMENSIONALITY REDUCTION WITH P RISK SCORES PER PERSON

After reviewing Multifactor Dimensionality Reduction(MDR) and its extensions, an approach to obtain P(larger than 1) risk scores is proposed to predict the continuous outcome for each subject. We study the mean square error(MSE) of dimensionality reduced models fitted with sets of 2 risk scores and...

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Main Author: Li, Ye
Format: Others
Published: UKnowledge 2018
Subjects:
Online Access:https://uknowledge.uky.edu/statistics_etds/34
https://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1040&context=statistics_etds
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spelling ndltd-uky.edu-oai-uknowledge.uky.edu-statistics_etds-10402019-10-16T04:26:39Z MULTIFACTOR DIMENSIONALITY REDUCTION WITH P RISK SCORES PER PERSON Li, Ye After reviewing Multifactor Dimensionality Reduction(MDR) and its extensions, an approach to obtain P(larger than 1) risk scores is proposed to predict the continuous outcome for each subject. We study the mean square error(MSE) of dimensionality reduced models fitted with sets of 2 risk scores and investigate the MSE for several special cases of the covariance matrix. A methodology is proposed to select a best set of P risk scores when P is specified a priori. Simulation studies based on true models of different dimensions(larger than 3) demonstrate that the selected set of P(larger than 1) risk scores outperforms the single aggregated risk score generated in AQMDR and illustrate that our methodology can determine a best set of P risk scores effectively. With different assumptions on the dimension of the true model, we considered the preferable set of risk scores from the best set of two risk scores and the best set of three risk scores. Further, we present a methodology to access a set of P risk scores when P is not given a priori. The expressions of asymptotic estimated mean square error of prediction(MSPE) are derived for a 1-dimensional model and 2-dimensional model. In the last main chapter, we apply the methodology of selecting a best set of risk scores where P has been specified a priori to Alzheimer’s Disease data and achieve a set of 2 risk scores and a set of three risk scores for each subject to predict measurements on biomarkers that are crucially involved in Alzheimer’s Disease. 2018-01-01T08:00:00Z text application/pdf https://uknowledge.uky.edu/statistics_etds/34 https://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1040&context=statistics_etds Theses and Dissertations--Statistics UKnowledge Multifactor Dimensionality Reduction Risk Score Continuous outcome Gene-gene Interaction Statistics and Probability
collection NDLTD
format Others
sources NDLTD
topic Multifactor Dimensionality Reduction
Risk Score
Continuous outcome
Gene-gene Interaction
Statistics and Probability
spellingShingle Multifactor Dimensionality Reduction
Risk Score
Continuous outcome
Gene-gene Interaction
Statistics and Probability
Li, Ye
MULTIFACTOR DIMENSIONALITY REDUCTION WITH P RISK SCORES PER PERSON
description After reviewing Multifactor Dimensionality Reduction(MDR) and its extensions, an approach to obtain P(larger than 1) risk scores is proposed to predict the continuous outcome for each subject. We study the mean square error(MSE) of dimensionality reduced models fitted with sets of 2 risk scores and investigate the MSE for several special cases of the covariance matrix. A methodology is proposed to select a best set of P risk scores when P is specified a priori. Simulation studies based on true models of different dimensions(larger than 3) demonstrate that the selected set of P(larger than 1) risk scores outperforms the single aggregated risk score generated in AQMDR and illustrate that our methodology can determine a best set of P risk scores effectively. With different assumptions on the dimension of the true model, we considered the preferable set of risk scores from the best set of two risk scores and the best set of three risk scores. Further, we present a methodology to access a set of P risk scores when P is not given a priori. The expressions of asymptotic estimated mean square error of prediction(MSPE) are derived for a 1-dimensional model and 2-dimensional model. In the last main chapter, we apply the methodology of selecting a best set of risk scores where P has been specified a priori to Alzheimer’s Disease data and achieve a set of 2 risk scores and a set of three risk scores for each subject to predict measurements on biomarkers that are crucially involved in Alzheimer’s Disease.
author Li, Ye
author_facet Li, Ye
author_sort Li, Ye
title MULTIFACTOR DIMENSIONALITY REDUCTION WITH P RISK SCORES PER PERSON
title_short MULTIFACTOR DIMENSIONALITY REDUCTION WITH P RISK SCORES PER PERSON
title_full MULTIFACTOR DIMENSIONALITY REDUCTION WITH P RISK SCORES PER PERSON
title_fullStr MULTIFACTOR DIMENSIONALITY REDUCTION WITH P RISK SCORES PER PERSON
title_full_unstemmed MULTIFACTOR DIMENSIONALITY REDUCTION WITH P RISK SCORES PER PERSON
title_sort multifactor dimensionality reduction with p risk scores per person
publisher UKnowledge
publishDate 2018
url https://uknowledge.uky.edu/statistics_etds/34
https://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1040&context=statistics_etds
work_keys_str_mv AT liye multifactordimensionalityreductionwithpriskscoresperperson
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