Document Type
Journal Article
Department/Unit
Department of Mathematics
Title
Maximum likelihood method for linear transformation models with cohort sampling data
Language
English
Abstract
Three widely used sampling designs-the nested case-control, case-cohort, and classical case-control designs-can be categorized as generalized case-cohort designs. Maximum likelihood methods are used to perform regression analysis of linear transformation models with these sampling designs, and the resulting estimator is proved to be consistent, asymptotically normal and semiparametrically efficient. Simulation studies and an application to the Stanford heart transplant data are presented.
Keywords
Linear transformation models, Maximum likelihood estimation, Missing at random, Nested case-control sampling
Publication Date
2015
Source Publication Title
Statistica Sinica
Volume
25
Issue
3
Start Page
1231
End Page
1248
Publisher
Academia Sinica, Institute of Statistical Science
DOI
10.5705/ss.2011.194
Link to Publisher's Edition
http://dx.doi.org/10.5705/ss.2011.194
ISSN (print)
10170405
ISSN (electronic)
19968507
APA Citation
Yao, Y. (2015). Maximum likelihood method for linear transformation models with cohort sampling data. Statistica Sinica, 25 (3), 1231-1248. https://doi.org/10.5705/ss.2011.194