A new Method of Estimating the Asymptotic Standard Error of the Hodges-Lehmann Estimator Based on Generalized Least Squares

A method of estimating the asymptotic standard error of the Hodges-Lehmann estimator based on generalized least squares is described. The results of a Monte Carlo study comparing the new method with existing ones are given.     

Kernel density estimation with binned data

Continuous data are often measured or used in binned or rounded form. In this paper we follow up on Hall's work analyzing the effect of using equally-spaced binned data in a kernel density estimator. It is shown that a surprisingly large amount of binning does not adversely affect the integrated mean squared error of a kernel estimate.     

AN ESTIMATE OF THE ASYMPTOTIC STANDARD ERROR OF THE SAMPLE MEDIAN

A method for estimating the asymptotic standard error of the sample median based on generalized least squares is outlined. The practical problems of implementing this new estimate along with those associated with two existing estimates are discussed. Finally a simulation study is presented to compare the three estimates.