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Improving Semiparametric Estimation ...

Fang, Fang.

Improving Semiparametric Estimation of Longitudinal Data with Covariance Function.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Improving Semiparametric Estimation of Longitudinal Data with Covariance Function./
Author:	Fang, Fang.
Description:	1 online resource (142 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9780355845853

Improving Semiparametric Estimation of Longitudinal Data with Covariance Function.
Fang, Fang.

Improving Semiparametric Estimation of Longitudinal Data with Covariance Function. - 1 online resource (142 pages)

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2018.

Includes bibliographical references

In this dissertation, we aim to improve efficiency of estimation in longitudinal data under generalized semi-parametric varying-coefficient models.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355845853Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

Improving Semiparametric Estimation of Longitudinal Data with Covariance Function.
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100 1 $a Fang, Fang. $3 1074648
245 1 0 $a Improving Semiparametric Estimation of Longitudinal Data with Covariance Function.
264 0 $c 2018
300 $a 1 online resource (142 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
500 $a Adviser: Yanqing Sun.
502 $a Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2018.
504 $a Includes bibliographical references
520 $a In this dissertation, we aim to improve efficiency of estimation in longitudinal data under generalized semi-parametric varying-coefficient models.
520 $a First, we investigate a profile weighted least square approach for model estimation by utilizing within subject correlations. Several methods for incorporating the within subject correlations are explored, including quasi-likelihood approach (QL), minimum generalized variance approach (MGV), the quadratic inference function approach (QIF) and newly proposed weighted least square approach (WLS). Our simulation study shows that the covariance assisted estimation is more efficient then working independence approach (WI).
520 $a Second, we apply the above methods to more complex generalized semiparametric varying-coefficient models that not only describe time-constant effects and time-varying effects as above but also model covariate-varying effects. The asymptotic properties of the estimators are derived theoretically. The simulation study demonstrates similar results as above.
520 $a The proposed estimation methods are applied to two real data sets. One is ACTG 244 clinical trial, the other is the STEP study with MITT cases. Both show that our methods by using correlation structure in estimation give more efficient estimation and provide more information about the data, and have broad applications in biomedical studies where within subject correlation often exists.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Biostatistics. $3 783654
650 4 $a Mathematics. $3 527692
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
690 $a 0308
690 $a 0405
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of North Carolina at Charlotte. $b Applied Mathematics. $3 1195601
773 0 $t Dissertation Abstracts International $g 79-08B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10751557 $z click for full text (PQDT)

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