Generalized linear models (Wedderburn and NeIder 1972, McCullagh and NeIder 1988) were introduced as a means of extending the techniques of ordinary parametric regression to several commonly-used regression models arising from non-normal likelihoods. Typically these models have a variance that depends on the mean function. However, in many cases the likelihood is unknown, but the relationship b...

Several nonparametric methods in a regression model are presented. First, the most classical ones: piecewise polynomial estimators, estimation with Spline bases, kernel estimators and projection estimators on orthonormal bases (such as Fourier or wavelet bases). Since these methods suffer from the curse of dimensionality, we also present Generalized Additive Models and CART regression models. T...

in this paper, we consider admissible estimation of the parameter ?r in the gamma distribution with truncated parameter space under entropy loss function. we obtain the classes of admissible estimators. the result can be applied to estimation of parameters in the normal, lognormal, pareto, generalized gamma, generalized laplace and other distributions.

We derive minimax generalized Bayes estimators of regression coefficients in the general linear model with spherically symmetric errors under invariant quadratic loss for the case of unknown scale. The class of estimators generalizes the class considered in Maruyama and Strawderman (2005) to include non-monotone shrinkage functions. AMS subject classification: Primary 62C20, secondary 62J07

In this article, we propose to estimate the regression parameters in a semiparametric generalized linear model by moment estimating equations. These estimators are shown to be consistent and asymptotically normal. We present two estimators of the nonparametric part, provide conditions for the existence and uniform consistency, and obtain faster rates of convergence under weaker assumptions.

Let y = Aβ + ε, where y is an N × 1 vector of observations, β is a p× 1 vector of unknown regression coefficients, A is an N × p design matrix and ε is a spherically symmetric error term with unknown scale parameter σ. We consider estimation of β under general quadratic loss functions, and, in particular, extend the work of Strawderman [J. Amer. Statist. Assoc. 73 (1978) 623–627] and Casella [A...

We consider the asymptotic analysis of penalized likelihood type estimators for generalized non-parametric regression problems in which the target parameter is a vector valued function defined in terms of the conditional distribution of a response given a set of covariates. A variety of examples including ones related to generalized linear models and robust smoothing are covered by the theory. ...

Iu this paper we study robust estimation in general models for the dependence of a response y on an explanatory vector z. We extend previous work on bounded influence estimators in linear regression. Second we construct optimal bounded influence estimators for generalized linear models. We consider the class of estimators defined by an estimating equation with a conditionally unbiased score flw...

Stuetzle and Mittal for ordinary nonparametric kernel regression and Kauermann and Tutz for nonparametric generalized linear model kernel regression constructed estimators with lower order bias than the usual estimators without the need for devices such as second derivative estimation and multiple bandwidths of di erent order We derive a similar estimator in the context of local multivariate es...

Quantile regression techniques have been widely used in empirical economics. In this paper, we consider the estimation of a generalized quantile regression model when data are subject to fixed or random censoring. Through a discretization technique, we transform the censored regression model into a sequence of binary choice models and further propose an integrated smoothed maximum score estimat...