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Estimating the mixing proportion in ...

Zhang, Xi.

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Estimating the mixing proportion in a semi-parametric mixture model from censored time-to-event data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Estimating the mixing proportion in a semi-parametric mixture model from censored time-to-event data./
Author:	Zhang, Xi.
Description:	61 p.
Notes:	Adviser: Dan Rabinowitz.
Contained By:	Dissertation Abstracts International67-02B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3203777
ISBN:	9780542524509

Estimating the mixing proportion in a semi-parametric mixture model from censored time-to-event data.
Zhang, Xi.

Estimating the mixing proportion in a semi-parametric mixture model from censored time-to-event data. - 61 p.

Adviser: Dan Rabinowitz.

Thesis (Ph.D.)--Columbia University, 2006.

In a semi-parametric mixture model H = theta F+(1-theta)G, a sample from the mixture of two unspecified distributions, and the samples from each of the mixing distributions are obtained. The goal here is to develop an approach to an efficient estimate of the mixing proportion theta. The problem is treated with the cases of uncensored and censored data. In both situations, a family of weighted estimating equations is presented, and the asymptotic behavior of the solution is examined. Then the optimal member of the family is derived. It is shown that the solution to the optimal estimating equation achieves the semi-parametric information bound.

ISBN: 9780542524509Subjects--Topical Terms:

517247
Statistics.

Estimating the mixing proportion in a semi-parametric mixture model from censored time-to-event data.
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035 $a (UnM)AAI3203777
035 $a AAI3203777
040 $a UnM $c UnM
100 1 $a Zhang, Xi. $3 1256056
245 1 0 $a Estimating the mixing proportion in a semi-parametric mixture model from censored time-to-event data.
300 $a 61 p.
500 $a Adviser: Dan Rabinowitz.
500 $a Source: Dissertation Abstracts International, Volume: 67-02, Section: B, page: 0968.
502 $a Thesis (Ph.D.)--Columbia University, 2006.
520 $a In a semi-parametric mixture model H = theta F+(1-theta)G, a sample from the mixture of two unspecified distributions, and the samples from each of the mixing distributions are obtained. The goal here is to develop an approach to an efficient estimate of the mixing proportion theta. The problem is treated with the cases of uncensored and censored data. In both situations, a family of weighted estimating equations is presented, and the asymptotic behavior of the solution is examined. Then the optimal member of the family is derived. It is shown that the solution to the optimal estimating equation achieves the semi-parametric information bound.
590 $a School code: 0054.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 0 $a Columbia University. $3 571054
773 0 $t Dissertation Abstracts International $g 67-02B.
790 $a 0054
790 1 0 $a Rabinowitz, Dan, $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3203777