Limiting distributions of the likelihood ratio test statistics for independence of normal random vectors

Mingyue Hu and Yongcheng Qi ()
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Mingyue Hu: University of Minnesota Duluth
Yongcheng Qi: University of Minnesota Duluth

Statistical Papers, 2023, vol. 64, issue 3, No 8, 923-954

Abstract: Abstract Consider the likelihood ratio test (LRT) statistics for the independence of sub-vectors from a p-variate normal random vector. We are devoted to deriving the limiting distributions of the LRT statistics based on a random sample of size n. It is well known that the limit is chi-square distribution when the dimension of the data or the number of the parameters are fixed. In a recent work by Qi et al. (Ann Inst Stat Math 71:911–946, 2019), it was shown that the LRT statistics are asymptotically normal under condition that the lengths of the normal random sub-vectors are relatively balanced if the dimension p goes to infinity with the sample size n. In this paper, we investigate the limiting distributions of the LRT statistic under general conditions. We find out all types of limiting distributions and obtain the necessary and sufficient conditions for the LRT statistic to converge to a normal distribution when p goes to infinity. We also investigate the limiting distribution of the adjusted LRT test statistic proposed in Qi et al. (2019). Moreover, we present simulation results to compare the performance of classical chi-square approximation, normal and non-normal approximation to the LRT statistics, chi-square approximation to the adjusted test statistic, and some other test statistics.

Keywords: Likelihood ratio test; Normal random vector; Central limit theorem; Chi-square approximation; Non-normal limit; High dimension; Independence; 62E20; 62H15 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00362-022-01348-2

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