A simple, direct approach is presented to approximate the optimal stopping rules associated with the Bayesian sequential test for a normal mean.

The local influence approach to the linear regression model with first-order autoregressive errors is developed and discussed. An advantage of this approach is that it avoids the inappropriate case-deletion diagnostic in the autoregressive model and it also allows simultaneous perturbations on...

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...

In nonlinear regression, we measure the interaction between observations in a random perturbation model for assessing the local influence. Our perturbation model perturbs all cases separately, and our measures combine all sides together. Approximations are given for these measures. An example of...

Inspired by Cook's (1986) assessment of local influence by studying the curvature of a surface associated with the overall discrepancy measure, this paper assesses the local influence through the curvature of the perturbation-formed surface of residual sum of squares (RSS) and multiple potential...