Assessing local influence in PLS regression by the second order approach

Partial least squares (PLS) regression has received increasing attention in recent years. However, like other regression methods, PLS fitting could be substantially altered by one or a few influential points. This paper assesses the local influence by examining the second order derivatives on certain perturbed parameter surfaces related to statistics of interest such as criterion and the estimated coefficients . We also illustrate our idea via a numerical example, where the masking phenomenon is present.