Suppose we have a random sample from a multinomial distribution with K parameters PI ' P2 ' •• • 'PK (,r Pi=1) • Three likelihood ratio tests about

Abstract: Likelihood ratio tests are intuitively appealing. Nevertheless, a number of examples are known in which they perform very poorly. The present paper discusses a large class of situations in which this is the case, and analyzes just how intuition misleads us; it also presents an alternative

Many statistical hypotheses can be formulated in terms of polynomial equalities and inequalities in the unknown parameters and thus correspond to semi-algebraic subsets of the parameter space. We consider large sample asymptotics for the likelihood ratio test of such hypotheses in models

This paper employs response surface regressions based on simulation experiments to calculate asymptotic distribution functions for the Johansen-type likelihood ratio tests for cointegration. These are carried out in the context of the models recently proposed by Pesaran, Shin, and Smith (1997

lesearch under Contract aAD.Y-. Reproduction in whole or in part is permitted for any purpose of the United States Government. AIR TOMl OWKCE OW1 =NI~rVC 3amm n:LOTE 01? TSPIMITTAL TO MIC This tachnilOl report hbs been reView"d A Is approved for public relesso 1AW AFI 190-1t. Distribut ion is U41iuitOd. UTm?. Dan E l p- _--- *.," A',ecosston o

We study the problem of testing for equality at a fixed point in the setting of nonparametric estimation of a monotone function. The likelihood ratio test for this hypothesis is derived in the particular case of interval censoring (or current status data) and its limiting distribution is obtained

) of a particular group. We propose a likelihood-ratio test that compares whether the best trees estimated with and without the constraint of monophyly are significantly different. Simulation suggests that the test is conservative when the null hypothesis (a particular specified constraint) is correct

regions of fixed volume based on

We present a new approach to study the Bahadur efficiency of likelihood tests. Our approach is based on the large deviation principle for empirical processes. We prove that the likelihood ratio test is Bahadur asymptotically optimal under mild sufficient conditions. Our results apply to common

Many statistical hypotheses can be formulated in terms of polynomial equalities and inequalities in the unknown parameters and thus correspond to semi-algebraic subsets of the parameter space. We consider large sample asymptotics for the likelihood ratio test of such hypotheses in models