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...

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...

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...

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...