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Statistical Methods for Multivariate Genetic Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Methods for Multivariate Genetic Analysis./
作者:	Ji, Soo Min.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	137 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
標題:	Biostatistics. -
ISBN:	9798438756651

Statistical Methods for Multivariate Genetic Analysis.
Ji, Soo Min.

Statistical Methods for Multivariate Genetic Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 137 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2022.

This item must not be sold to any third party vendors.

This dissertation develops statistical and computational methods for human genetics. We consider modern solutions to estimate the power of proposed genetic studies, and propose an alternative to the mixed model framework for analysis on non-Gaussian distributions. The methods we develop are designed for multivariate simulation and analysis in high dimensions. We implement our methods in individual, open-sourced Julia packages. They are freely available to the scientific community through the OpenMendel platform.

ISBN: 9798438756651Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Human genetics

Statistical Methods for Multivariate Genetic Analysis.
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300 $a 137 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Lange, Kenneth L.;Zhou, Hua.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation develops statistical and computational methods for human genetics. We consider modern solutions to estimate the power of proposed genetic studies, and propose an alternative to the mixed model framework for analysis on non-Gaussian distributions. The methods we develop are designed for multivariate simulation and analysis in high dimensions. We implement our methods in individual, open-sourced Julia packages. They are freely available to the scientific community through the OpenMendel platform.
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