Title: Robust Regression with Density Power Divergence: Theory, Comparisons, and Data Analysis
Citation: ENTROPY vol. 22 no. 4 p. 1-17
Publisher: MDPI
Publication Year: 2020
JRC N°: JRC120358
ISSN: 1099-4300 (online)
URI: https://www.mdpi.com/1099-4300/22/4/399
DOI: 10.3390/e22040399
Type: Articles in periodicals and books
Abstract: Minimum density power divergence estimation provides a general framework for robust statistics depending on a parameter a which determines the robustness properties of the method. The usual estimation method is numerical minimization of the power divergence. The paper considers the special case of linear regression. We develop an alternative estimation procedure using the methods of S-estimation. The rho function so obtained is proportional to one minus a suitably scaled normal density raised to the power a. We use the theory of S-estimation to determine the asymptotic efficiency and breakdown point for this new form of S-estimation. Two sets of comparisons are made. In one, S power divergence is compared with other S-estimators using four distinct rho functions. Plots of efficiency against breakdown point show that the properties of S power divergence are close to those of Tukey’s biweight. The second set of comparisons is between S power divergence estimation and numerical minimization. Monitoring these two procedures in terms of breakdown point shows that the numerical minimization yields a procedure with larger robust residuals and a lower empirical breakdown point, thus providing an estimate of a leading to more efficient parameter estimates.
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