We consider the problem of estimating quantile regression coefficients in errors-in-variables models. When the error variables for both the response and the manifest variables have a joint distribution that is spherically symmetric but otherwise unknown, the regression quantile estimates based...

There have been studies on how the asymptotic efficiency of a nonparametric function estimator depends on the handling of the within-cluster correlation when nonparametric regression models are used on longitudinal or cluster data. In particular, methods based on smoothing splines and local...

This paper considers an extension of M-estimators in semiparametric models for independent observations to the case of longitudinal data. We approximate the nonparametric function by a regression spline, and any M-estimation algorithm for the usual linear models can then be used to obtain...

The minimum Hellinger distance estimator is known to have desirable properties in terms of robustness and efficiency. We propose an approximate minimum Hellinger distance estimator by adapting the approach to grouped data from a continuous distribution. It is easier to compute the approximate...

The Markov chain marginal bootstrap (MCMB) was introduced by He and Hu [2002. Markov chain marginal bootstrap. J. Amer. Statist. Assoc. 97(459) (2002) 783-795] as a bootstrap-based method for constructing confidence intervals or regions for a wide class of M-estimators in linear regression and...

The conflict between high breakdown and efficiency needs to be interpreted with care and understood in connection with the versatility of both concepts in statistical estimation.

We consider S-estimators of multivariate location and common dispersion matrix in multiple populations. Instead of averaging the robust estimates of the individual covariance matrices, as used by Todorov, Neykov and Neytchev (1990), the observations are pooled for estimating the common...

When a linear model is used to analyze spatially correlated data, but the form of the spatial correlogram is unknown or difficult to specify, it is not straightforward to carry out valid statistical inference on regression parameters. We derive the asymptotic distributions for a class of...

We propose a location estimator based on a convex linear combination of the sample mean and median. The main attraction is the conceptual simplicity and transparency, but it remains very competitive in performance for a wide range of distributions. The estimator aims at minimizing the asymptotic...