ÂÛÎÄÕªÒª£ºStatistical estimation and inference for the marginal hazard models with varying-coefficients for multivariate failure time data are important subjects in survival analysis. A local pseudo-partial likelihood procedure is proposed for estimating the unknown coefficient functions. A weighted average estimator is also proposed in an attempt to improve the efficiency of the estimator. Consistency and the asymptotic normality of the proposed estimators are established and standard error formulas for the estimated coefficients are derived and empirically tested. To reduce the computational burden of the maximum local pseudo-partial likelihood estimator, a simple and useful one-step estimator is proposed. Statistical properties of the one-step estimator are established and simulation studies are conducted to compare the performance of the one-step estimator to the maximum local pseudo-partial likelihood estimator. The results show that the one-step estimator can save computational cost without eteriorating its performance both asymptotically and empirically and that the optimal weighted average estimator is more efficient than the maximum local pseudo-partial likelihood estimator. A data set from the Busselton Population Health Surveys is analyzed to illustrate our proposed methodology.

ÂÛÎÄÌâÄ¿: Marginal hazard models with varying-coefficients for multivariate failure time data

ÂÛÎÄ×÷Õß: Cai, J., Fan, J. Zhou, H. and Zhou, Y

·¢±í¿¯Îï: Annals of Statistics, 35£¨1£©£¬324-354