RECENT ADVANCE IN SENSITIVITY ANALYSIS IN MULTIVARIATE STATISTICAL METHODS

Methodologies have been developed in the last two decades for detecting influential observations and evaluating the stability of the results of analysis not only in regression and related methods but also in other multivariate methods. In developing these methodologies influence functions play important roles. The present paper shows that influence functions can be derived in various multivariate statistical methods and that a general strategy based on influence functions and its robust version are useful for detecting singly and/or jointly influential observations. Cases of principal component analysis, exploratory factor analysis and confirmatory factor analysis are studied with numerical examples.