Download Robustness in Statistics by Robert L. Launer, Graham N. Wilkinson PDF

By Robert L. Launer, Graham N. Wilkinson

ISBN-10: 0124381502

ISBN-13: 9780124381506

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And Welsch, R. E. (1977). Robust regression using iteratively reweighted least squares. Commun. , A6, 813-828. 7. Huber, P. J. (1964). Robust estimation of a location parameter. Ann. Math. , 35, 73-101. 8. Huber, P. J. (1977). Robust Statistical Procedures, Society for Industrial and Applied Mathematics, Philadelphia. 9. Jaeckel, L. A. (1972). Estimating regression coefficients by minimizing the dispersion of the residuals. Ann. Math. , 43, 1449-1458. 10. Jureckov~, J. (1977). Asymptotic relations of M-estimates and R-estimates in linear regression models.

A systematic search for optimally robust choices for y(•) and k( ) has not yet been implemented, but certain ad hoc choices have been investigated empirically by means of simulations. In particular, y's of the form g(c) _ 1 1+x2 + 2 a + bx 2 3 ) (1+x , for various choices of a and b, have been used. The rationale for the choice of the functional form was to approximate the normal density in the central region and to provide a slow approach to zero in the tails. 5), have been found to yield interesting results.

The author was partially supported by National Institute of Health Grant GM 22271-02. Department of Statistics The University of Iowa Iowa City, Iowa 52242 ROBUSTNESS IN STATISTICS The Robustness of Residual Displays D. F. Andrews 1. Introduction Linear models represent a large component of statistical analysis both directly and as approximations to non-linear models. These models were the first widespread applications of the current growth of robust estimation procedures. Robust regression procedures and robust analogues of analysis of variance are being implemented and used in many fields.

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