B0165
Title: Robust estimation for general transformation models with random effects
Authors: Yuanyuan Lin - The Chinese University of Hong Kong (Hong Kong) [presenting]
Abstract: The semiparametric transformation models with random effects are useful in analyzing dependent data, for example, recurrent data and clustered data. With the error and random effect distributions specified, it has been previously proved that the nonparametric maximum likelihood estimators (NPMLE) are semiparametric efficient. We consider a more general class of transformation models with random effects, under which an unknown monotonic transformation of the response is linearly related to the covariates and the random effects with unspecified error and random effect distributions. This class of models is broad enough to include many popular models and allows various random effect distributions. We propose an estimator based on the maximum rank correlation, which does not reply on any further model assumption except the symmetry of the random effect distribution. The consistency and asymptotic normality of the proposed estimator is established. A random weighting resampling scheme is employed for inference. Moreover, the proposed method can be easily extended to handle censored data and clustered data. Numerical studies demonstrate that the proposed method performs well in practical situations. An application is illustrated with the Framingham cholesterol data.
