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Comparison of high-dimensional Bayesian variable selection methods with application in genetics

Show simple item record 2022-06-15T08:49:14Z 2022-06-15T08:49:14Z 2022-06-15
dc.title Comparison of high-dimensional Bayesian variable selection methods with application in genetics en
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty.URI Helsingin yliopisto fi University of Helsinki en Helsingfors universitet sv
dct.creator Laiho, Aleksi
dct.issued 2022 xx
dct.abstract In statistics, data can often be high-dimensional with a very large number of variables, often larger than the number of samples themselves. In such cases, selection of a relevant configuration of significant variables is often needed. One such case is in genetics, especially genome-wide association studies (GWAS). To select the relevant variables from high-dimensional data, there exists various statistical methods, with many of them relating to Bayesian statistics. This thesis aims to review and compare two such methods, FINEMAP and Sum of Single Effects (SuSiE). The methods are reviewed according to their accuracy of identifying the relevant configurations of variables and their computational efficiency, especially in the case where there exists high inter-variable correlations within the dataset. The methods were also compared to more conventional variable selection methods, such as LASSO. The results show that both FINEMAP and SuSiE outperform LASSO in terms of selection accuracy and efficiency, with FINEMAP producing sligthly more accurate results with the expense of computation time compared to SuSiE. These results can be used as guidelines in selecting an appropriate variable selection method based on the study and data. en
dct.subject statistics
dct.subject variable selection
dct.subject bayesian
dct.subject gwas
ethesis.isPublicationLicenseAccepted true
ethesis.language englanti fi
ethesis.language English en
ethesis.language engelska sv
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
ethesis.thesistype pro gradu-avhandlingar sv
dct.identifier.ethesis E-thesisID:631ac5d5-0a52-4511-93a8-52f6b1cd3c34
dct.identifier.urn URN:NBN:fi:hulib-202206152701
dct.alternative Suuriulotteisten bayesilaisten muuttujanvalintamenetelmien vertailu sovellettuna genetiikkaan fi
ethesis.facultystudyline Tilastotiede fi
ethesis.facultystudyline Statistics en
ethesis.facultystudyline Statistik sv
ethesis.mastersdegreeprogram Matematiikan ja tilastotieteen maisteriohjelma fi
ethesis.mastersdegreeprogram Master 's Programme in Mathematics and Statistics en
ethesis.mastersdegreeprogram Magisterprogrammet i matematik och statistik sv

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