Empirical likelihood ratio tests with power one

Sieve Empirical Likelihood Ratio Tests for Nonparametric Functions

Generalized likelihood ratio statistics have been proposed in Fan, Zhang and Zhang [Ann. Statist. 29 (2001) 153–193] as a generally applicable method for testing nonparametric hypotheses about nonparametric functions. The likelihood ratio statistics are constructed based on the assumption that the distributions of stochastic errors are in a certain parametric family. We extend their work to the...

On likelihood ratio tests

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 approach which in these situations is optimal. 1. The popularity of likelihood ratio tests Faced with a new...

Likelihood Ratio Tests

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 that satisfy standard probabilistic regularity conditions. We show that the assumptions of Chernoff's theore...

Likelihood Ratio Tests for

Likelihood Ratio Tests

Previously, we have employed a program (Jtest) to test an hypothesis involving many parameters (multiple constraints). This test, which involves comparing parameter estimates with hypothesized values, is known as a Wald test. We also used the Jtest program to compare two di¤erent estimators in a Hausman test. There is an alternative method for conducting joint tests, which can be implemented ea...