Language:
English
繁體中文
Help
回圖書館首頁
手機版館藏查詢
Login
Search
Recommendations
ReaderScope
My Account
Help
Simple Search
Advanced Search
Public Library Lists
Public Reader Lists
AcademicReservedBook [CH]
BookLoanBillboard [CH]
BookReservedBillboard [CH]
Classification Browse [CH]
Exhibition [CH]
New books RSS feed [CH]
Personal Details
Saved Searches
Recommendations
Borrow/Reserve record
Reviews
Personal Lists
ETIBS
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Applications of Bayesian nonparametr...
~
Li, Li.
Linked to FindBook
Google Book
Amazon
博客來
Applications of Bayesian nonparametrics to reliability and survival data.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Applications of Bayesian nonparametrics to reliability and survival data./
Author:
Li, Li.
Description:
118 p.
Notes:
Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.
Contained By:
Dissertation Abstracts International75-09B(E).
Subject:
Statistics. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3620973
ISBN:
9781303917066
Applications of Bayesian nonparametrics to reliability and survival data.
Li, Li.
Applications of Bayesian nonparametrics to reliability and survival data.
- 118 p.
Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.
Thesis (Ph.D.)--University of South Carolina, 2014.
This item is not available from ProQuest Dissertations & Theses.
Reliability and survival data are widely encountered across many common settings. Subjects under investigation often include machines, bioassays, patients, etc.; their reliability or survival distribution, and its association with covariate processes, are commonly of interest. Within this dissertation, the first two chapters focus on reliability data where repairable systems fail and get interventions, e.g. repairs in the event process. It begins with a nonparametric test for the commonly assumed ''good as old'' assumption for minimal repair models and then a semi-parametric regression model is introduced for reliability data using Kijima's effective age. The third chapter focuses on survival data observed with potential spatial correlation. We first develop a Bayesian semi-parametric approach to the extended hazard model and then extend this framework to allow for spatial correlation among survival times. In contrast to widely used frailty models, our approach preserves marginal interpretations. Flexible modeling approaches in the Bayesian context are used for baseline failure rate or hazard and Markov chain Monte Carlo techniques to obtain the posterior inferences. The proposed tests and models are examined in several simulation studies and applications.
ISBN: 9781303917066Subjects--Topical Terms:
517247
Statistics.
Applications of Bayesian nonparametrics to reliability and survival data.
LDR
:02210nmm a2200277 4500
001
2065314
005
20151130143849.5
008
170521s2014 ||||||||||||||||| ||eng d
020
$a
9781303917066
035
$a
(MiAaPQ)AAI3620973
035
$a
AAI3620973
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Li, Li.
$3
1036040
245
1 0
$a
Applications of Bayesian nonparametrics to reliability and survival data.
300
$a
118 p.
500
$a
Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.
500
$a
Adviser: Timothy Hanson.
502
$a
Thesis (Ph.D.)--University of South Carolina, 2014.
506
$a
This item is not available from ProQuest Dissertations & Theses.
520
$a
Reliability and survival data are widely encountered across many common settings. Subjects under investigation often include machines, bioassays, patients, etc.; their reliability or survival distribution, and its association with covariate processes, are commonly of interest. Within this dissertation, the first two chapters focus on reliability data where repairable systems fail and get interventions, e.g. repairs in the event process. It begins with a nonparametric test for the commonly assumed ''good as old'' assumption for minimal repair models and then a semi-parametric regression model is introduced for reliability data using Kijima's effective age. The third chapter focuses on survival data observed with potential spatial correlation. We first develop a Bayesian semi-parametric approach to the extended hazard model and then extend this framework to allow for spatial correlation among survival times. In contrast to widely used frailty models, our approach preserves marginal interpretations. Flexible modeling approaches in the Bayesian context are used for baseline failure rate or hazard and Markov chain Monte Carlo techniques to obtain the posterior inferences. The proposed tests and models are examined in several simulation studies and applications.
590
$a
School code: 0202.
650
4
$a
Statistics.
$3
517247
690
$a
0463
710
2
$a
University of South Carolina.
$b
Statistics.
$3
1681787
773
0
$t
Dissertation Abstracts International
$g
75-09B(E).
790
$a
0202
791
$a
Ph.D.
792
$a
2014
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3620973
based on 0 review(s)
Location:
ALL
電子資源
Year:
Volume Number:
Items
1 records • Pages 1 •
1
Inventory Number
Location Name
Item Class
Material type
Call number
Usage Class
Loan Status
No. of reservations
Opac note
Attachments
W9298024
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
On shelf
0
1 records • Pages 1 •
1
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login