Three common classes of kernel regression estimators are considered: the Nadaraya–Watson (NW) estimator, Priestley–Chao (PC) and Gasser–Müller (GM) estimator. It is shown that (i) GM estimator has a certain monotonicity preservation property for any K, (ii) NW this if only K log concave, (iii) PC does not have K. Other related properties these discussed.

In this paper it is shown that using the maximum likelihood ML prin ciple for the estimation of multivariate probit models leads to consistent and normally distributed pseudo maximum likelihood regression parame ter estimators PML estimators even if the true correlation structure of the responses is misspeci ed As a consequence e g the PML estimator of the random e ects probit model may be used...

Mutual information may be used to select the embedding lag of a time series. However, this lag selection is usually limited to the analysis of the mutual information between a pair of lagged values in the series. In this paper, generalized mutual information estimators are proposed to take into account more than two variables in the lag selection. Experimental results show that lag selection us...

In this paper a Maximum Likelihood Estimator is derived in the Generalized Linear Model-based regression profiles under the monotonic change in Phase II. The performance of proposed estimator is comprehensively investigated through some special cases and compared to estimators under step change and drift. The results show that the proposed estimator has better performance in small and medium sh...

Two simple resistant regression estimators with OP (n−1/2) convergence rate are presented. Ellipsoidal trimming can be used to trim the cases corresponding to predictor variables x with large Mahalanobis distances, and the forward response plot of the residuals versus the fitted values can be used to detect outliers. The first estimator uses ten forward response plots corresponding to ten diffe...

We propose a Bayesian Penalized Spline Predictive (BPSP) estimator for a finite population proportion in an unequal probability sampling setting. This new method allows the probabilities of inclusion to be directly incorporated into the estimation of a population proportion, using a probit regression of the binary outcome on the penalized spline of the inclusion probabilities. The posterior pre...

In many situations, data follow a generalized partly linear model in which the mean of the responses is modeled, through a link function, linearly on some covariates and nonparametrically on the remaining ones. A new class of robust estimates for the smooth function η, associated to the nonparametric component, and for the parameter β, related to the linear one, is defined. The robust estimator...

Estimators of regression coefficients are known to be asymptotically normally distributed, provided certain regularity conditions are satisfied. In small samples and if the noise is not normally distributed, this can be a poor guide to the quality of the estimators. The paper addresses this problem for small and medium sized samples and heavy tailed noise. In particular, we assume that the nois...