Identification of a Distributional Model

John A. Taylor; Anthony J. Jakeman

doi:10.1080/03610918508812453

Identification of a Distributional Model

John A. Taylor
, Anthony J. Jakeman

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

15 Citations (Scopus)

Abstract

Methods for selecting a distributional model for a random variable such as ambient air quality concentration are examined. Specific consideration is given to identification of a model from the exponential, lognormal, Weibull and gamma distributions. The performance of a likelihood ratio statistic and a Kolmogorov ratio statistic are examined by Monte Carlo simulation. On the basis of these results a procedure for increasing the probability of correct selection is proposed.

Original language	English
Pages (from-to)	497-508
Number of pages	12
Journal	Communications in Statistics Part B: Simulation and Computation
Volume	14
Issue number	2
DOIs	https://doi.org/10.1080/03610918508812453
Publication status	Published - 1 Jan 1985

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AB - Methods for selecting a distributional model for a random variable such as ambient air quality concentration are examined. Specific consideration is given to identification of a model from the exponential, lognormal, Weibull and gamma distributions. The performance of a likelihood ratio statistic and a Kolmogorov ratio statistic are examined by Monte Carlo simulation. On the basis of these results a procedure for increasing the probability of correct selection is proposed.

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KW - likelihood function

KW - lognormal distribution

KW - normal distribution weibull distribution

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DO - 10.1080/03610918508812453

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JO - Communications in Statistics Part B: Simulation and Computation

JF - Communications in Statistics Part B: Simulation and Computation

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ER -

Identification of a Distributional Model

Abstract

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