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Master's Dissertation
Full name
Djidenou Hans Amos Montcho
Knowledge Area
Date of Defense
São Carlos, 2021
Milan, Luis Aparecido (President)
Saraiva, Erlandson Ferreira
Tekougang, Thierry Chekouo
Title in English
Bayesian variable selection using Data Driven Reversible Jump: an application to schizophrenia data
Keywords in English
Bayesian inference
Informed reversible jump
Variable selection
Abstract in English
Symptom based diagnosis are known to be limited specially concerning complex disorders such as schizophrenia. Modern attempts in providing predictive risk for such disease, to assist existing diagnosis tools, integrate genetic and brain information in what is known as imaging genetics. In this monography, our goal is both inferential and predictive. Regarding the inference, given the functional Magnetic Resonance Image and the Single Nucleotide Polymorphisms information of people diagnosed with schizophrenia and healthy people, we use a Bayesian probit model to select discriminating variables, while to estimate the predictive risk, the most promising models are combined using a Bayesian model averaging scheme. For these purposes, we propose an adaptive reversible jump markov chain monte carlo, named data driven reversible jump, for selecting the variables, estimating their effects and the predictive risk for future subjects.
Title in Portuguese
Seleção Bayesiana de variáveis usando o algoritmo de saltos reversíveis direcionado pelos dados: uma aplicação a dados de esquizofrenia
Keywords in Portuguese
Algoritmo de saltos reversíveis
Inferência Bayesiana
Seleção de variáveis
Abstract in Portuguese
Diagnósticos médicos baseados em sintomas são conhecidos por suas limitações, especialmente no entendimento de distúrbios complexos como esquizofrenia. Abordagens modernas e complementares para predizer o risco de tais doenças integram dados genômicos e cerebrais. Nesta monografia, nosso objetivo é inferencial e preditivo. Na inferência, com base em dados de ressonância magnética funcional e de polimorfismo de nucleotídeo único obtidos de pessoas saudáveis e diagnosticadas com esquizofrenia, utilizamos um modelo probito Bayesiano para selecionar as variáveis mais importantes a fim de discriminar os pacientes. Para estimar o risco preditivo, os modelos mais promissores são combinados usando a ponderação bayesiana de modelos. Para estas finalidades, propomos o algoritmo de saltos reversíveis orientado pelos dados para realizar a seleção de variáveis, estimação de parâmetros dos modelos e predição para futuros pacientes.
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