TY - JOUR
T1 - Maximum likelihood method for linear transformation models with cohort sampling data
AU - YAO, Yuan
N1 - Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2015/7
Y1 - 2015/7
N2 - 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.
AB - 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.
KW - Linear transformation models
KW - Maximum likelihood estimation
KW - Missing at random
KW - Nested case-control sampling
UR - http://www3.stat.sinica.edu.tw/statistica/J25N3/J25N321/J25N321.html
UR - https://www.jstor.org/stable/24721229
UR - http://www.scopus.com/inward/record.url?scp=84990061912&partnerID=8YFLogxK
U2 - 10.5705/ss.2011.194
DO - 10.5705/ss.2011.194
M3 - Journal article
AN - SCOPUS:84990061912
SN - 1017-0405
VL - 25
SP - 1231
EP - 1248
JO - Statistica Sinica
JF - Statistica Sinica
IS - 3
ER -