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Analyses of Viral Genetic Networks in the Presence of Missing Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Analyses of Viral Genetic Networks in the Presence of Missing Data./
作者:	Vu, Tyler.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	81 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
標題:	Biostatistics. -
ISBN:	9798834049074

Analyses of Viral Genetic Networks in the Presence of Missing Data.
Vu, Tyler.

Analyses of Viral Genetic Networks in the Presence of Missing Data. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 81 p.

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2022.

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

Molecular epidemiology is increasingly used to investigate patterns of HIV transmission. To do so, many analyses consider investigating properties of a sexual or transmission network. The use of sampled data to estimate such properties is a common practice; however, in the presence of missing data, even missing completely at random, networks based on sampled data do not represent their population counterparts. As a result, inferences on sampled networks become unreliable. To address this challenge, we propose statistical approaches to accommodating missing data in the analysis of sampled networks.

ISBN: 9798834049074Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Viral genetic netowrks

Analyses of Viral Genetic Networks in the Presence of Missing Data.
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100 1 $a Vu, Tyler. $3 3689127
245 1 0 $a Analyses of Viral Genetic Networks in the Presence of Missing Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 81 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
500 $a Advisor: Tu, Xin.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Molecular epidemiology is increasingly used to investigate patterns of HIV transmission. To do so, many analyses consider investigating properties of a sexual or transmission network. The use of sampled data to estimate such properties is a common practice; however, in the presence of missing data, even missing completely at random, networks based on sampled data do not represent their population counterparts. As a result, inferences on sampled networks become unreliable. To address this challenge, we propose statistical approaches to accommodating missing data in the analysis of sampled networks.
590 $a School code: 0033.
650 4 $a Biostatistics. $3 1002712
653 $a Viral genetic netowrks
653 $a Missing data
653 $a Human immunodeficiency virus
690 $a 0308
710 2 0 $a University of California, San Diego. $b Biostatistics. $3 3689128
773 0 $t Dissertations Abstracts International $g 84-01B.
790 $a 0033
791 $a Ph.D.
792 $a 2022
793 $a English