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dc.creatorAavani, Pooya
dc.date.accessioned2020-07-09T17:49:55Z
dc.date.available2020-07-09T17:49:55Z
dc.date.created2019-12
dc.date.issued2019-12
dc.date.submittedDecember 2019
dc.identifier.urihttps://hdl.handle.net/2346/86154
dc.description.abstractNumerous statistical methods have been developed to explore genomic imprinting and maternal effects, which are causes of parent-of-origin patterns in complex human diseases. However, most of them either only model one of these two confounded epigenetic effects, or make strong yet unrealistic assumptions about the population to avoid over- parameterization. A recent partial likelihood method (LIME) can identify both epigenetic effects based on case-control family data without those assumptions. Theoretical and empirical studies have shown its validity and robustness. However, because LIME obtains parameter estimation by maximizing partial likelihood, it is interesting to compare its efficiency with full likelihood maximizer. To overcome the difficulty in over-parameterization when using full likelihood, in this study we propose a Monte Carlo Expectation Maximization (MCEM) method to detect imprinting and maternal effects jointly. Those unknown mating type probabilities, the nuisance parameters, can be considered as latent variables in EM algorithm. Monte Carlo samples are used to numerically approximate the expectation function that cannot be solved algebraically. Our simulation results show that this MCEM algorithm takes longer computational time, and can give higher bias in some simulations compared to LIME. However, it can generally detect both epigenetic effects with higher power and smaller standard error which demonstrates that it can be a good complement of LIME method.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.subjectStatistical model
dc.subjectMonte Carlo expectation maximization algorithm
dc.subjectImprinting effects-maternal effect
dc.titleDetecting imprinting and maternal effects using Monte Carlo expectation maximization algorithm
dc.typeThesis
dc.date.updated2020-07-09T17:49:55Z
dc.type.materialtext
thesis.degree.nameMaster of Science
thesis.degree.levelMasters
thesis.degree.disciplineStatistics
thesis.degree.grantorTexas Tech University
thesis.degree.departmentMathematics and Statistics
dc.contributor.committeeMemberRice, Sean
dc.contributor.committeeChairZhang, Fangyuan
dc.contributor.committeeChairTrindade, Alex
dc.rights.availabilityRestricted to TTU only. For access, please log in at the top of this page using your eRaider credentials.


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