Research on Sensitivity Analysis in Multivariate Methods

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In these two years we focused on the following four topics in sensitivity analysis in multivariate methods : 1) Practical methods for detecting influential subsets by eliminating the so-called masking effect, 2) Development of a sensitivity analysis procedure in PCA bsed on a subset of variables, 3) Development of a sensitivity analysis procedure in partial least squares regression analysis (PLS) , and 4) Statistical software for sensitivity analysis in multivariate methods. For the first topic we proposed a robust sensitivity analysis procedure in COMPESTAT'94 paper, and also discussed the relationship between our procedure and Cook's local influence approach in Tanaka (1994) and in the presentation in a selected topic contributed paper session in ISI,Beijing. Concerning the second topic we have fully discussed in an invited paper presented in ICSQP'95 at Seoul and in Tanaka and Mori's paper which will appear in American Journal of Mathematical and Management Sciences. For the third topic we have derived influence functions in PLS and proposed a method of sensitivity analysis procedure. After presented in the fifth Japan-China Symposium on Statistics in 1994 we are now writing a full paper on this topic. As statistical software we have published SACS/B (Watadani and Tanaka, 1994) , which can detect not only singly but also jointly influential observations in covariance structure analysis including confirmatory factor analysis, exploratory factor analysis and LISREL models.