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Communications in Statistics - Theory and Methods Volume 23, 1994 - Issue 5
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Original Articles

Multivariate mutual information

Sampling distribution with applications

José-luis Guerrero Business School-Georgetown University, Room G-2, Washington, D.C, 20057
Pages 1319-1339 | Received 01 Feb 1993, Published online: 27 Jun 2007

Citations (9)

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José-Luis Guerrero-Cusumano. (1998) Measures of dependence for the multivariate t distribution with applications to the stock market. Communications in Statistics - Theory and Methods 27:12, pages 2985-3006.
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Articles from other publishers (8)

Taylor Rowe & Troy Day. (2019) The Sampling Distribution of the Total Correlation for Multivariate Gaussian Random Variables. Entropy 21:10, pages 921.
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Giuseppe Vinci, Valérie Ventura, Matthew A. Smith & Robert E. Kass. (2018) Adjusted regularization of cortical covariance. Journal of Computational Neuroscience 45:2, pages 83-101.
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Kai Zhang, Kaixiang Peng & Yuri A. W. Shardt. (2018) A Comparison of Different Statistics for Detecting Multiplicative Faults in Multivariate Statistics-Based Fault Detection Approaches. IEEE Access 6, pages 43808-43823.
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Kai Zhang, Yuri A. W. Shardt, Zhiwen Chen, Steven X. Ding & Kaixiang Peng. (2016) A brief survey of different statistics for detecting multiplicative faults in multivariate statistical process monitoring. A brief survey of different statistics for detecting multiplicative faults in multivariate statistical process monitoring.
S. Bersimis, S. Psarakis & J. Panaretos. (2006) Multivariate statistical process control charts: an overview. Quality and Reliability Engineering International 23:5, pages 517-543.
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Douglas J. Miller & Wei-han Liu. (2002) On the recovery of joint distributions from limited information. Journal of Econometrics 107:1-2, pages 259-274.
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José-Luis Guerrero-Cusumano. (1996) A test for population collinearity a Kullback-Leibler information approach. Information Sciences 92:1-4, pages 295-311.
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José-Luis Guerrero-Cusumano. (1995) Testing variability in multivariate quality control: A conditional entropy measure approach. Information Sciences 86:1-3, pages 179-202.
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