Paper Info
Title
Exact Distribution And High-Dimensional Asymptotics For Improperness Test Of Complex Signals
Abstract
Improperness testing for complex-valued vectors and processes has been of interest lately due to the potential applications of complex-valued time series analysis in several research areas. This paper provides exact distribution characterization of the GLRT (Generalized Likelihood Ratio Test) statistics for Gaussian complex-valued signals under the null hypothesis of properness. This distribution is a special case of the Wilks's lambda distribution, as are the distributions of the GLRT statistics in multivariate analysis of variance (MANOVA) procedures. In the high dimensional setting, i.e. when the size of the vectors grows at the same rate as the number of samples, a closed form expression is obtained for the asymptotic distribution of the GLRT statistics. This is, to our knowledge, the first exact characterization for the GLRT-based improperness testing.
Year
DOI
Venue
2019
10.1109/icassp.2019.8683518
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
Complex signals, improperness, GLRT, high-dimensional statistics, Wilks's Lambda distribution
Applied mathematics,Time series,Likelihood-ratio test,Pattern recognition,Computer science,Closed-form expression,Gaussian,High-dimensional statistics,Artificial intelligence,Asymptotic analysis,Asymptotic distribution,Wilks's lambda distribution
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Florent Chatelain100.68
Nicolas Le Bihan225423.35