The detection and subsequent treatment of influential observations have been well covered with respect to ordinary least squares (OLS) under an assumed multiple linear regression (MLR) model using measures such as Cook's Distance. However, OLS can be shown to be a useful method under a much...

Principal Component Analysis (PCA) is an important tool in multivariate analysis, in particular when faced with high dimensional data. There has been much done with regard to sensitivity analysis and the development of influence diagnostics for the eigenvector estimators that define the sample...

The first goal of this article is to consider influence analysis of principal Hessian directions (pHd) and highlight how such an analysis can provide valuable insight into its behaviour. Such insight includes reasons as to why pHd can sometimes return informative results when it is not expected...

Sliced inverse regression, sliced inverse regression II and sliced average variance estimation are three related dimension-reduction methods that require relatively mild model assumptions. As an approximation for the relative influence of single observations from large samples, the influence...

A finite sample estimate of the variance of the sample median is proposed. This estimate is shown to have smaller bias than the related estimate of Maritz and Jarrett (1978) and Efron (1979).

Confidence intervals for the population median based on interpolating adjacent order statistics are presented. They are shown to depend only slightly on the underlying distribution. A simple, nonlinear interpolation formula is given which works well for a broad collection of underlying...