Parameter estimation is the first step in constructing any control chart. Most estimators of mean and dispersion are sensitive to the presence of outliers. The data may be contaminated by outliers either locally or globally. The exciting robust estimators deal only with global contamination. In this paper a robust estimator for dispersion is proposed to reduce the effect of local contamination ...

Gupta and Shabbir (2008) have suggested an alternative form of ratio-type estimator for estimating the population mean. In this paper, we introduced new estimators by mixing two, stratified and cluster sampling method. Then we improved these estimators by using auxiliary variables and introducing new estimators. For sampling in infinite populations with a high geographic dispersion, the populat...

In this paper, we propose a new componentwise estimator of a dispersion matrix, based on a highly robust estimator of scale. The key idea is the elimination of a location estimator in the dispersion estimation procedure. The robustness properties are studied by means of the influence function and the breakdown point. Further characteristics such as asymptotic variance and efficiency are also an...

The traditional class of elliptical distributions is extended to allow for asymmetries. A completely robust dispersion matrix estimator (the ‘spectral estimator’) for the new class of ‘generalized elliptical distributions’ is presented. It is shown that the spectral estimator corresponds to an M-estimator proposed by Tyler (1983) in the context of elliptical distributions. Both the generalizati...

Process control charts are proven techniques for improving quality. Specifying the control limits is the most important step in designing a control chart. The presence of outliers may extremely affect the estimates of parameters using classical methods. Robust estimators which are not affected by outliers or the small departures from the model assumptions are applied in this paper to specify th...

This paper presents a simple resistant estimator of multivariate location and dispersion. The DD plot is a plot of Mahalanobis distances from the classical estimator versus the distances from a resistant estimator and can be used to detect outliers and as a diagnostic for multivariate normality. The new estimator can be used in the DD plot, is easy to compute and provides insights about several...

The dispersion parameter of a chi-distributed radial error is of interest in numerous target analysis problems as a measure of weapon-system accuracy, and it is often of practical importance to estimate it. This paper presents a few classical estimators including the maximum likelihood estimator, an unbiased estimator and a minimum mean squared error estimator of this dispersion for both when t...

For the Gamma family generalized linear models, the dispersion parameter is contained in the variance of the model parameter estimator. So it will affect the results of statistical inference or any kinds of tests that refer to variance. This paper reviewed several existing estimators of dispersion parameter via the Monte Carlo experiments to see which one is to be preferred when the sample size...

In this paper, we derive the in uence function of dispersion estimators, based on a scale approach. The relation between the gross-error sensitivity of dispersion estimators and the one of the underlying scale estimator is described. We show that for the bivariate Gaussian distributions, the asymptotic variance of covariance estimators is minimal in the independent case, and is strictly increas...