A Central Limit Theorem in Non-parametric Regression with Truncated, Censored and Dependent Data

作者：Liang Han Ying^*; de Una Alvarez Jacobo; del Carmen Iglesias Perez Maria

来源：Scandinavian Journal of Statistics, 2015, 42(1): 256-269.

DOI：10.1111/sjos.12105

• asymptotic normality
• conditional mean function
• Nadaraya-Watson and local linear smoothing
• truncated and censored data
• -mixing

On the basis of the idea of the Nadaraya-Watson (NW) kernel smoother and the technique of the local linear (LL) smoother, we construct the NW and LL estimators of conditional mean functions and their derivatives for a left-truncated and right-censored model. The target function includes the regression function, the conditional moment and the conditional distribution function as special cases. It is assumed that the lifetime observations with covariates form a stationary -mixing sequence. Asymptotic normality of the estimators is established. Finite sample behaviour of the estimators is investigated via simulations. A real data illustration is included too.

• 出版日期2015-3
• 单位同济大学