A Note on the Generation of Random Normal Deviates
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This article is published in Annals of Mathematical Statistics.The article was published on 1958-06-01 and is currently open access. It has received 3235 citations till now.read more
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Measures of multivariate skewness and kurtosis with applications
TL;DR: In this article, the authors developed measures of multivariate skewness and kurtosis by extending certain studies on robustness of the t statistic, and the asymptotic distributions of the measures for samples from a multivariate normal population are derived and a test for multivariate normality is proposed.
Journal ArticleDOI
Non-Uniform Random Variate Generation.
B. J. T. Morgan,Luc Devroye +1 more
TL;DR: This chapter reviews the main methods for generating random variables, vectors and processes in non-uniform random variate generation, and provides information on the expected time complexity of various algorithms before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.
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Non-uniform random variate generation
TL;DR: A survey of the main methods in non-uniform random variate generation can be found in this article, where the authors provide information on the expected time complexity of various algorithms, before addressing modern topics such as indirectly specified distributions, random processes and Markov chain methods.
Journal ArticleDOI
Robust Tests for the Equality of Variances
Morton B. Brown,Alan B. Forsythe +1 more
TL;DR: In this paper, alternative formulations of Levene's test statistic for equality of variances are found to be robust under nonnormality, using more robust estimators of central location in place of the mean.
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Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models
George E. P. Box,David A. Pierce +1 more
TL;DR: In this paper, it is shown that the residual autocorrelations are to a close approximation representable as a singular linear transformation of the auto-correlations of the errors so that they possess a singular normal distribution.