regression model. Asymptotic distribution theory is developed for the estimation method which enjoys the same rate of convergence …

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 … ; Nonparametrie Regression …

In a single index Poisson regression model with unknown link function, the index parameter can be root-n consistently … regression ; kernel estimation ; bandwidth selection …

Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very … regression models. Therefore, I adapt some robust methods, such as least trimmed squares, to nonlinear and limited …, as well as examples of regression models which the proposed estimators can be applied to. -- least trimmed squares …

We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression … likelihood method is developed. It is shown that when missing responses are imputed using the semiparametric regression method …

performed telephone interviews on a ross-sectional random population sample of 1124 French women who had been pregnant between … regression and generalised partial linear models, a class of semiparametric regression models. Our final random effect logistic … regression model predicted that, among women aged 25 years, the odds-ratio (OR) of spontaneous abortion associated with the male …