This article describes the asymptotic properties of local polynomial regression estimators for univariate and additive models when observation weights are included. The implications of these ndings are discussed for local scoring estimators, a widely used class of estimators for generalized additive models described in Hastie and Tibshirani (1990).

A general notion of universal consistency of nonparametric estimators is introduced that applies to regression estimation, conditional median estimation, curve fitting, pattern recognition, and learning concepts. General methods for proving consistency of estimators based on minimizing the empirical error are shown. In particular, distribution-free almost sure consistency of neural network esti...

This paper considers estimation of a "xed-e!ects version of the generalized regression model of Han (1987, Journal of Econometrics 35, 303}316). The model allows for censoring, places no parametric assumptions on the error disturbances, and allows the "xed e!ects to be correlated with the covariates. We introduce a class of rank estimators that consistently estimate the coe$cients in the genera...

The unified hybrid censoring is a mixture of generalized Type-I and Type-II hybrid censoring schemes. This article presents the statistical inferences on Generalized Exponential Distribution parameters when the data are obtained from the unified hybrid censoring scheme. It is observed that the maximum likelihood estimators can not be derived in closed form. The EM algorithm for computing the ma...

‎Recently‎, ‎a new distribution‎, ‎named as extended generalized exponential distribution‎, ‎has been introduced by Kundu and Gupta (2011). ‎In this paper‎, ‎we consider the extended generalized exponential distribution with known shape parameters α and β. ‎At first‎, ‎the exact expressions for marginal and product moments of o...

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. ...

We introduce a new class of robust estimators for generalized linear models which is an extension of the class of projection estimators for linear regression. These projection estimators are defined using an initial robust estimator for a generalized linear model with only one unknown parameter. We found a bound for the maximum asymptotic bias of the projection estimator caused by a fraction ε ...

In this paper we consider a panel data model with error components that are both spatially and time-wise correlated. The model blends specifications typically considered in the spatial literature with those considered in the error components literature. We introduce generalizations of the generalized moments estimators suggested in Kelejian and Prucha (1999. A generalized moments estimator for ...