Abstract
For random samples of size n obtained from m-variate normal distributions, we investigate the classical likelihood ratio tests for their means and covariance matrices in the high-dimensional case. Many researchers analyze the test statistics for the case of n going to infinity and m keeping fixed. In the high-dimensional setting, the objective of this paper is to prove that the likelihood ratio test statistics converge in distribution to normal distributions when both m and n go to infinity with m/n→y∈(0,1]. We obtain this conclusion very intuitively by using Lyapunov’s central limit theorem.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
