Feature selection is fundamental for modeling the high-dimensional data, where the number of features can be huge and much larger than the sample size. Since the feature space is so large, many traditional procedures become numerically infeasible. It is hence essential to first remove most...

We consider a finite mixture model with "k" components and a kernel distribution from a general one-parameter family. The problem of testing the hypothesis "k"=2 "versus""k"⩾3 is studied. There has been no general statistical testing procedure for this problem. We propose a modified likelihood...