Facultade de Fisioterapia

A Central Limit Theorem in Nonparametric Regression with Truncated, Censored and Dependent Data

Liang, Han-Ying; de Uña-Álvarez, Jacobo; Iglesias Pérez, María del Carmen
Abstract:
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.
Year:
2015
Type of Publication:
Article
Keywords:
asymptotic normality; conditional mean function; Nadaraya Watson and local linear smoothing; truncated and censored data
Journal:
Scandinavian Journal of Statistics
Volume:
42
Pages:
256-269
Month:
March
DOI:
http://dx.doi.org/10.1111/sjos.12105
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