Accession Number : AD0749053
Title : Unbiasedness in Linear Model Estimation.
Descriptive Note : Themis optimization research program,
Corporate Author : TEXAS A AND M UNIV COLLEGE STATION INST OF STATISTICS
Personal Author(s) : Sielken,R. L. , Jr. ; Hartley,H. O.
Report Date : AUG 1972
Pagination or Media Count : 15
Abstract : Consider the estimation of beta in the familiar linear model y = X beta + e. It is well-known that the least squares estimation principle gives rise to an unbiased estimator (beta bar) under very mild conditions. Two alternative estimation principles sometimes considered are the minimization of the sum of absolute residuals and the maximum absolute residual. Since these principles will usually not lead to a unique estimator, the latter will depend on the linear programming algorithm used for its computation. The authors develop two algorithms which are based upon these alternative principles and yield, under very general conditions, unbiased estimators. (Author)
Descriptors : (*REGRESSION ANALYSIS, LEAST SQUARES METHOD), LINEAR PROGRAMMING, RANDOM VARIABLES, MATRICES(MATHEMATICS), ALGORITHMS
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE