SUMMARY Generalised weighted Cramer-von Mises distance estimators in an arbitrary model with a k-dimensional parameter vector are investigated. The distance function is defined as a function G of model-based residuals for a specified target model F and the empirical cumulative distribution function F, over the real line, invoking a weight function w. It is shown that the estimator is Fisher consistent, asymptotically multivariate normal, and nearly efficient with desirable robustness properties. If the true model is equal to the target model, the residual function G does not affect the limiting distribution. The weight function w controls the asymptotic distribution and the robustness of the estimator. Three different classes of the weight functions are introduced for different outlier patterns. These weight functions produce estimators asymptotically as efficient as the maximum likelihood estimators at the true model. An alternative way of calculating the estimators is considered. Simulation results indicate that asymptotic results are useful for moderate sample sizes and that the estimators are stable at the neighbourhood of the target model.
[1]
Dennis D. Boos,et al.
Minimum Distance Estimators for Location and Goodness of Fit
,
1981
.
[2]
T. Wet,et al.
On minimum cramer-von mises-norm parameter estimation
,
1981
.
[3]
B. Lindsay.
Efficiency versus robustness : the case for minimum Hellinger distance and related methods
,
1994
.
[4]
W. R. Schucany,et al.
Minimum Distance and Robust Estimation
,
1980
.
[5]
M. Silvapulle,et al.
Minimum mean squared estimation of location and scale parameters under misspecification of the model
,
1981
.
[6]
D. Donoho,et al.
Pathologies of some Minimum Distance Estimators
,
1988
.