The trade-off between robustness and efficiency and the effect of model smoothing in minimum disparity inference
- 1 September 1994
- journal article
- research article
- Published by Taylor & Francis in Journal of Statistical Computation and Simulation
- Vol. 50 (3-4) , 173-185
- https://doi.org/10.1080/00949659408811609
Abstract
Through an empirical study at the normal model it is shown that the curvature parameter of the residual adjustment function (Lindsay 1994) is not always an adequate global measure of the trade-off between robustness and efficiency of the minimum disparity estimators. Our study shows that the estimator obtained by minimizing the negative exponential disparity is an attractive robust estimator with good efficiency properties. Smoothing the model with the same kernel used to determine the nonparametric density estimator results in higher efficiency for the minimum disparity estimators, especially for the estimator of the scale parameter. In addition the disparity tests (including the negative exponential disparity test) are shown to be good robust alternatives to the likelihood ratio test at the normal model.Keywords
This publication has 5 references indexed in Scilit:
- Minimum hellinger distance estimation for normal modelsJournal of Statistical Computation and Simulation, 1991
- Hellinger Deviance Tests: Efficiency, Breakdown Points, and ExamplesJournal of the American Statistical Association, 1989
- Minimum Hellinger Distance Estimation for the Analysis of Count DataJournal of the American Statistical Association, 1987
- Minimum Hellinger Distance Estimation for Multivariate Location and CovarianceJournal of the American Statistical Association, 1986
- Minimum Hellinger Distance Estimates for Parametric ModelsThe Annals of Statistics, 1977