Laplace approximations for hypergeometric functions with Hermitian matrix argument

Ronald W. Butler and Andrew T.A. Wood

Journal of Multivariate Analysis, 2022, vol. 192, issue C

Abstract: We develop highly accurate Laplace approximations for two hypergeometric functions of Hermitian matrix argument denoted by 1F̃1 and 2F̃1. These functions arise naturally in the multivariate noncentral distribution theory for complex multivariate normal models used in wireless communication, radar, sonar, and seismic detection. Each function arises as a factor in the moment generating function (MGF) for the noncentral distribution of the log-likelihood ratio statistic: 1F̃1 is a factor for complex MANOVA testing and 2F̃1 is a factor when testing block independence of complex normal signals. We use simulation to show the excellent accuracy achieved by the two approximations. We also compute ROC curves for these two tests by inverting the noncentral MGFs with saddlepoint approximations after replacing the true hypergeometric functions with their Laplace approximations.

Keywords: Block independence; Complex MANOVA; Complex multivariate normal distribution; Hypergeometric functions; MIMO communication; Power function; ROC curve; Signal detection; Wireless communication (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.jmva.2022.105087

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