Department of Mathematics
Maximum likelihood method for linear transformation models with cohort sampling data
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.
Linear transformation models, Maximum likelihood estimation, Missing at random, Nested case-control sampling
Source Publication Title
Academia Sinica, Institute of Statistical Science
Link to Publisher's Edition
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