On Box–Muller Transformation and Simulation of Normal Record Data

N. Balakrishnan; H. Y. So; X. J. Zhu

doi:10.1080/03610918.2014.950872

On Box–Muller Transformation and Simulation of Normal Record Data

N. Balakrishnan^*, H. Y. So, X. J. Zhu

^*Corresponding author for this work

McMaster University

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

Abstract

Record data are commonly encountered in many fields such as sports, geography, finance, and reliability. In this article, we use the well-known Box–Muller transformation to develop an efficient method of simulating record data from the normal distribution. Another method based on exponential records is also discussed. Then, the performance of these algorithms is compared with some standard simulation methods.

Original language	English
Pages (from-to)	3670-3682
Number of pages	13
Journal	Communications in Statistics: Simulation and Computation
Volume	45
Issue number	10
DOIs	https://doi.org/10.1080/03610918.2014.950872
Publication status	Published - 25 Nov 2016
Externally published	Yes

Keywords

Best linear unbiased estimates
Box–Muller algorithm
Kolmogorov–Smirnov test
Lower records
Upper records
Variance-covariance matrix

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10.1080/03610918.2014.950872

Cite this

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AB - Record data are commonly encountered in many fields such as sports, geography, finance, and reliability. In this article, we use the well-known Box–Muller transformation to develop an efficient method of simulating record data from the normal distribution. Another method based on exponential records is also discussed. Then, the performance of these algorithms is compared with some standard simulation methods.

KW - Best linear unbiased estimates

KW - Box–Muller algorithm

KW - Kolmogorov–Smirnov test

KW - Lower records

KW - Upper records

KW - Variance-covariance matrix

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JF - Communications in Statistics: Simulation and Computation

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ER -

On Box–Muller Transformation and Simulation of Normal Record Data

Abstract

Keywords

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