A
Limit Theory for Likelihood Ratio Test under Unidentifiability
for General Dependent Processes Yuewu Xu Fordham University Abstract
In many important practical hypothesis testing
problems about substructures of the population, the regularity
conditions of the
classical likelihood ratio test theory fail to hold, due to a lack of
identifiability of the parameters under the null hypothesis and other
serious violations. Under mild regularity conditions, this paper
establishes the limit theory for the likelihood ratio tests under
unidentiability for general dependent processes. Practical
applications include testing for the number of components in mixture
models, testing for the order of ARMA models, testing for the order of
Hidden Markov Models, etc.
Co-authors: Yongzhao Shao, New York University |