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Phase Transition for Multiple Testing : A Simulation Based Comprehensive Study of Bayesian and Frequentist Methods

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dc.date.accessioned 2016-10-21T05:23:20Z und
dc.date.accessioned 2017-10-24T12:22:06Z
dc.date.available 2016-10-21T05:23:20Z und
dc.date.available 2017-10-24T12:22:06Z
dc.date.issued 2016-10-21T05:23:20Z
dc.identifier.uri http://radr.hulib.helsinki.fi/handle/10138.1/5814 und
dc.identifier.uri http://hdl.handle.net/10138.1/5814
dc.title Phase Transition for Multiple Testing : A Simulation Based Comprehensive Study of Bayesian and Frequentist Methods en
ethesis.discipline Applied Mathematics en
ethesis.discipline Soveltava matematiikka fi
ethesis.discipline Tillämpad matematik sv
ethesis.discipline.URI http://data.hulib.helsinki.fi/id/2646f59d-c072-44e7-b1c1-4e4b8b798323
ethesis.department.URI http://data.hulib.helsinki.fi/id/61364eb4-647a-40e2-8539-11c5c0af8dc2
ethesis.department Institutionen för matematik och statistik sv
ethesis.department Department of Mathematics and Statistics en
ethesis.department Matematiikan ja tilastotieteen laitos fi
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty.URI http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca
ethesis.university.URI http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97
ethesis.university Helsingfors universitet sv
ethesis.university University of Helsinki en
ethesis.university Helsingin yliopisto fi
dct.creator Toivonen, Karoliina
dct.issued 2016
dct.language.ISO639-2 eng
dct.abstract Multiple testing is a statistical inference problem, applied widely in the fields of genomic studies, QTL mapping and national security, where large number of hypotheses is being tested simultaneously. However, it is not always straightforward whether multiple testing can successfully be carried out for a specific dataset. To measure whether multiple testing works as desired, the error rate, defined as P ( Type I error ) + P ( Type II error ) , for investigating performance of different frequentist and Bayesian testing methods is considered. In a grid of all possible combinations of p (proportion of signal in the data) and τ^2 /σ^2 (variance), a simulation study is conducted, testing a set of hypotheses with Benjamini-Hochberg procedure and its modified versions, as well as with Parametric Empirical and Full Bayes methods. As a result, a sharp phase transition phenomenon for the error rate of each of the inference schemes is noticed, indicating the existence of a phase boundary defining regions of p and τ^2 /σ^2 for which multiple testing is feasible and for which it isn't. This discovery is then also discussed from the point of view of variable selection; Bayesian methods in variable selection are expected to show similar results due to the well known connections between multiple testing and model selection. Furthermore, the outcome of the simulation yields differences between the performance of full Bayes and empirical Bayes methods noted before from asymptotic point of view. This finding is then contemplated from the perspective of phase boundary, resulting in new ideas of how to avoid conflict when using parametric Bayes approaches as an approximation of full Bayes analysis. en
dct.language en
ethesis.language.URI http://data.hulib.helsinki.fi/id/languages/eng
ethesis.language English en
ethesis.language englanti fi
ethesis.language engelska sv
ethesis.thesistype pro gradu-avhandlingar sv
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
ethesis.thesistype.URI http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis
dct.identifier.urn URN:NBN:fi-fe2017112251214
dc.type.dcmitype Text

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