论文信息 - Confidence Intervals for Bisquare Regression Estimates

Confidence Intervals for Bisquare Regression Estimates

Abstract This paper describes the results from a Monte Carlo study of robust regression confidence-interval estimation in the model y = a + bx + e. Bisquare estimators were studied on samples of 11 and 21 with five design matrices. Four estimators of scale were used to form confidence intervals. Samples were generated from two distributions: normal and slash. For many design matrices, the four scale estimators give almost equally efficient confidence intervals but require different t values to achieve this efficiency. However, for each scale estimator, the t value changes little as a function of which linear combination of the parameters is being estimated.

A. M. Gross

[1] L. Denby,et al. Robust regression estimators compared via monte carlo , 1977 .

[2] A. M. Gross. Confidence Interval Robustness with Long-Tailed Symmetric Distributions , 1976 .

[3] D. F. Andrews,et al. A Robust Method for Multiple Linear Regression , 1974 .

[4] A Monte Carlo Study of Two Robust Alternatives of Least Square Regression Estimation , 1974 .

[5] J. Tukey,et al. The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data , 1974 .

[6] A. M. Gross. A Monte Carlo Swindle for Estimators of Location , 1973 .

[7] P. J. Huber. Robust Regression: Asymptotics, Conjectures and Monte Carlo , 1973 .

[8] Paul W. Holland. Monte Carlo for Robust Regression: The Swindle Unmasked , 1973 .

[9] A. M. Gross. A robust confidence interval for location for symmetric, long-tailed distributions. , 1973, Proceedings of the National Academy of Sciences of the United States of America.

[10] W. Rogers,et al. Understanding some long-tailed symmetrical distributions , 1972 .

[11] Daniel A. Relles. Variance Reduction Techniques for Monte Carlo Sampling from Student Distributions , 1970 .

[12] P. Bickel. On Some Robust Estimates of Location , 1965 .

[13] P. J. Huber. Robust Estimation of a Location Parameter , 1964 .

[14] G. Box,et al. Robustness to non-normality of regression tests , 1962 .