Quasi-universal bandwidth selection for kernel density estimators

The problem of confidence estimation of a normal mean vector when data on different subsets of response variables are missing is considered. A simple approximate confidence region is proposed when the data matrix is of monotone pattern. Simultaneous inferential procedures based on Scheffeʹs method and Bonferroniʹs method are outlined. Further, applications of the results to a repeated measurements model are given. The results are illustrated using a pratical example.