Conditional Likelihood Ratio Test for a Nonnegative Normal Mean Vector
针对多元均值非负的假设检验问题，推导了条件似然比检验，证明其比无条件似然比检验更有效，并通过模拟和临床试验数据验证了其优势。
Abstract The conditional likelihood ratio test is derived for significance of a multivariate mean having nonnegative components. This test is shown to be uniformly more powerful than the unconditional likelihood ratio test derived by Perlman. The computation involved in the new test is a straightforward programming task. Simulation results suggest that this test is also uniformly more powerful than a half-space test proposed by Tang and Hotelling's T 2 test. The consistency, invariance and unbiasedness of the new test are established, and the test is illustrated with data from a randomized clinical trial.