Title: Multi-parameter One-Sided Monitoring Test,
Abstract: Multi-parameter one-sided hypothesis test problems arise naturally in many
applications. We are particularly interested in effective tests for monitoring
multiple quality indices in forestry products. Our search reveals that there
are many effective statistical methods in the literature for normal data, and
that they can easily be adapted for non-normal data. We find that the beautiful
likelihood ratio test is unsatisfactory, because in order to control the size,
it must cope with the least favorable distributions at the cost of power. In
this paper, we find a novel way to slightly ease the size control, obtaining a
much more powerful test. Simulation confirms that the new test retains good
control of the type I error and is markedly more powerful than the likelihood
ratio test as well as many competitors based on normal data. The new method
performs well in the context of monitoring multiple quality indices.