
Accession Number : AD0664508
Title : ROBUST REGRESSION BY MODIFIED LEASTSQUARES.
Descriptive Note : Technical rept.,
Corporate Author : YALE UNIV NEW HAVEN CONN DEPT OF STATISTICS
Personal Author(s) : Relles,D. A.
Report Date : DEC 1967
Pagination or Media Count : 136
Abstract : Estimates of regression parameters are usually found by minimizing the sum of squared differences between observed and predicted values of a dependent variable. As is well known, such estimates can be seriously impaired by the presence of outliers. To combat this effect, I consider minimizing an alternative function of differences. This function is the square for arguments less than a certain value (determined from the data itself) and linear for arguments beyond that. An algorithm for computing the estimate is given, largesample properties are derived, and smallsample properties are studied by means of Monte Carlo exploration of various error distributions. An extended summary of results is given. (Author)
Descriptors : (*REGRESSION ANALYSIS, *LEAST SQUARES METHOD), MONTE CARLO METHOD, ITERATIONS, SAMPLING, DISTRIBUTION FUNCTIONS, STATISTICAL ANALYSIS, MAPPING(TRANSFORMATIONS), RANDOM VARIABLES, THEOREMS, ALGORITHMS, THESES
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE