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Small sample inference for exponenti...

Randrianampy, Noroharivelo Volaniaina.

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Small sample inference for exponential survival times with heavy right-censoring.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Small sample inference for exponential survival times with heavy right-censoring./
Author:	Randrianampy, Noroharivelo Volaniaina.
Description:	93 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.
Contained By:	Dissertation Abstracts International74-07B(E).
Subject:	Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3537401
ISBN:	9781267970770

Small sample inference for exponential survival times with heavy right-censoring.
Randrianampy, Noroharivelo Volaniaina.

Small sample inference for exponential survival times with heavy right-censoring. - 93 p.

Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.

Thesis (Ph.D.)--Missouri University of Science and Technology, 2012.

We develop a saddlepoint-based method and several generalized Bartholomew methods for generating confidence intervals about the rate parameter of an exponential distribution in the presence of heavy random right-censoring. Butler's conditional moment generating function formula is used to derive the relevant moment generating function for the rate parameter score function which provides access to a saddlepoint-based bootstrap method. Moment generating functions also play a key role in the generalized Bartholomew methods we develop. Since heavy censoring is assumed, the possible non-existence of the rate parameter maximum likelihood estimate (MLE) is nonignorable. The overwhelming majority of existing methods condition upon the event that the number of observed failures is non-zero (rate parameter MLE exists). With heavy censoring, these methods may not be able to produce confidence interval an appreciable percentage of times. Our proposed methods are unconditional in the sense that they can produce confidence intervals even when the rate parameter MLE does not exist. The unconditional saddlepoint method in particular defaults in a natural way to a proposed generalized Bartholomew method when the rate parameter MLE fails to exist. We find that the proposed saddlepoint method outperforms competing Bartholomew methods in the presence of heavy censoring and small sample sizes.

ISBN: 9781267970770Subjects--Topical Terms:

515831
Mathematics.

Small sample inference for exponential survival times with heavy right-censoring.
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020 $a 9781267970770
035 $a (MiAaPQ)AAI3537401
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100 1 $a Randrianampy, Noroharivelo Volaniaina. $3 2093230
245 1 0 $a Small sample inference for exponential survival times with heavy right-censoring.
300 $a 93 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-07(E), Section: B.
500 $a Adviser: Robert Paige.
502 $a Thesis (Ph.D.)--Missouri University of Science and Technology, 2012.
520 $a We develop a saddlepoint-based method and several generalized Bartholomew methods for generating confidence intervals about the rate parameter of an exponential distribution in the presence of heavy random right-censoring. Butler's conditional moment generating function formula is used to derive the relevant moment generating function for the rate parameter score function which provides access to a saddlepoint-based bootstrap method. Moment generating functions also play a key role in the generalized Bartholomew methods we develop. Since heavy censoring is assumed, the possible non-existence of the rate parameter maximum likelihood estimate (MLE) is nonignorable. The overwhelming majority of existing methods condition upon the event that the number of observed failures is non-zero (rate parameter MLE exists). With heavy censoring, these methods may not be able to produce confidence interval an appreciable percentage of times. Our proposed methods are unconditional in the sense that they can produce confidence intervals even when the rate parameter MLE does not exist. The unconditional saddlepoint method in particular defaults in a natural way to a proposed generalized Bartholomew method when the rate parameter MLE fails to exist. We find that the proposed saddlepoint method outperforms competing Bartholomew methods in the presence of heavy censoring and small sample sizes.
590 $a School code: 0587.
650 4 $a Mathematics. $3 515831
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Statistics. $3 517247
690 $a 0405
690 $a 0364
690 $a 0463
710 2 $a Missouri University of Science and Technology. $b Mathematics. $3 2093231
773 0 $t Dissertation Abstracts International $g 74-07B(E).
790 $a 0587
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3537401