Some Statistical Tests Based on $$\mathfrak{N}$$ -Distances

Svetlozar T. Rachev, Lev B. Klebanov, Stoyan V. Stoyanov and Frank J. Fabozzi
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Svetlozar T. Rachev: Stony Brook University, Department of Applied Mathematics and Statistics College of Business
Lev B. Klebanov: Charles University, Department of Probability and Statistics
Stoyan V. Stoyanov: EDHEC Business School EDHEC-Risk Institute
Frank J. Fabozzi: EDHEC Business School EDHEC-Risk Institute

Chapter Chapter 24 in The Methods of Distances in the Theory of Probability and Statistics, 2013, pp 581-588 from Springer

Abstract: Abstract In this chapter, we construct statistical tests based on the theory of $$\mathfrak{N}$$ -distances. We consider a multivariate two-sample test, a test to determine if two distributions belong to the same additive type, and tests for multivariate normality with unknown mean and covariance matrix.

Keywords: Covariance Matrix; Random Vector; Simulated Sample; Additive Type; Multivariate Normality (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-4869-3_24

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DOI: 10.1007/978-1-4614-4869-3_24

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