Accession Number : AD0745377
Title : Improved Density Estimation.
Descriptive Note : Technical rept.,
Corporate Author : SOUTHERN METHODIST UNIV DALLAS TEX DEPT OF STATISTICS
Personal Author(s) : Sommers,John P.
Report Date : 28 FEB 1972
Pagination or Media Count : 90
Abstract : Non-parametric estimation of a continuous probability density function almost always leads to a biased estimator. The purpose of the paper is to attack the problem of bias reduction. The problem is approached by using combinations of estimators of the form studied by Parzen (1962). Combining more than one of these estimators by the jackknife method of Schucany, Gray, and Owen (1971), new estimators are formed which generally have a substantial decrease in bias. The paper studies the properties of these new estimators in detail. Approximations are derived for their variance and bias. General classes of these new estimators are shown to be asymptotically unbiased and mean square consistent. Furthermore, the estimators are shown to be asymptotically better than the original estimators using mean square error as a criterion. (Author)
Descriptors : (*STATISTICAL ANALYSIS, *PROBABILITY DENSITY FUNCTIONS), SAMPLING, RANDOM VARIABLES, DISTRIBUTION FUNCTIONS, THEOREMS
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE