In this paper we define a kernel estimator of the conditional density for a left-truncated and right-censored model based on the generalized product-limit estimator of the conditional distributed function. Under the observations with multivariate covariates form a stationary α-mixing sequence,...

In this paper we derive the asymptotic normality and a Berry-Esseen type bound for the kernel conditional density estimator proposed in Ould-Saïd and Cai (2005) [26] when the censored observations with multivariate covariates form a stationary [alpha]-mixing sequence.

In this paper we derive rates of uniform strong convergence for the kernel estimator of the regression function in a left-truncation model. It is assumed that the lifetime observations with multivariate covariates form a stationary [alpha]-mixing sequence. The estimation of the covariate's...

In this paper, we discuss the estimation of a density function based on censored data by the kernel smoothing method when the survival and the censoring times form a stationary [alpha]-mixing sequence. A Berry-Esseen type bound is derived for the kernel density estimator at a fixed point x. For...

Consider the nonparametric regression model Yni=g(xni)+[epsilon]ni for i=1,...,n, where g is unknown, xni are fixed design points, and [epsilon]ni are negatively associated random errors. Nonparametric estimator gn(x) of g(x) will be introduced and its asymptotic properties are studied. In...

In this paper we define a new nonlinear wavelet-based estimator of conditional density function for a random left truncation and right censoring model. We provide an asymptotic expression for the mean integrated squared error (MISE) of the estimator. It is assumed that the lifetime observations...