We introduce a kernel-based estimator of the density function and regression function for data that have been grouped …

nonparametric marker dependent hazard models. We work with a full counting process framework that allows for left truncation and …

We propose a new estimator for nonparametric regression based on local likelihood estimation using an estimated error … score function obtained from the residuals of a preliminary nonparametric regression. We show that our estimator is … kernel estimators when the error distribution is not normal. We investigate the finite sample performance of our procedure on …

Smooth nonparametric kernel density and regression estimators are studied when the data is strongly dependent. In … nonparametric kernel regression estimators are studied. One important and surprising characteristic found is that its asymptotic … particular, we derive Central (and Noncentral) Limit Theorems for the kernel density estimator of a multivariate Gaussian process …

The nonparametric censored regression model, with a fixed, known censoring point (normalized to zero), is y = max[0,m … estimators of m(x) and its derivatives. The convergence rate is the same as for an uncensored nonparametric regression and its … heteroscedasticity. We also extend the estimator to the nonparametric truncated regression model, in which only uncensored data points …

We investigate a new separable nonparametric model for time series, which includes many ARCH models and AR models …

dependence introduces a nonparametric aspect, and we discuss choice of such numbers. We then consider some possibilities for … nonparametric estimates of probability densities and regression functions, where dependence often has no effect on first …

is dynamic through some linear filters. A special case ofthis is a nonparametric regression with serially correlated …