Consider the regression model y = beta 0 1 + Xbeta + epsilon. Recently, the Liu estimator, which is an alternative biased estimator beta L (d) = (X'X + I) -1 (X'X + dI)beta OLS , where 0d1 is a parameter, has been proposed to overcome multicollinearity . The advantage of beta L (d) over the...

Many methods have been developed for detecting multiple outliers in a single multivariate sample, but very few for the case where there may be groups in the data. We propose a method of simultaneously determining groups (as in cluster analysis) and detecting outliers, which are points that are...

We propose a robust method for estimating principal functions based on MM estimation. Specifically, we formulate functional principal component analysis into alternating penalized M-regression with a bounded loss function. The resulting principal functions are given as M-type smoothing spline...

Classification of samples into two or multi-classes is to interest of scientists in almost every field. Traditional statistical methodology for classification does not work well when there are more variables (p) than there are samples (n) and it is highly sensitive to outlying observations. In...

In this study, the method of local influence, which was introduced by Cook as a general tool for assessing the influence of local departures from the underlying assumptions, is applied to ridge regression, by defining the maximum pseudo-likelihood ridge estimator obtained using the augmentation...

Since the seminal paper by Cook (1977) in which he introduced Cook's distance, the identification of influential observations has received a great deal of interest and extensive investigation in linear regression. It is well documented that most of the popular diagnostic measures that are based...

Leverage values are being used in regression diagnostics as measures of unusual observations in the <italic>X</italic>-space. Detection of high leverage observations or points is crucial due to their responsibility for masking outliers. In linear regression, high leverage points (HLP) are those that stand far...