Accession Number : AD0664508

Title :   ROBUST REGRESSION BY MODIFIED LEAST-SQUARES.

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, large-sample properties are derived, and small-sample 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