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Mixture Lognormal Approximations to Lognormal Sum Distributions
IEEE Communications Letters, 2007In wireless communication, co-channel interference is usually characterized by a sum of lognormal random variables. Since calculating the exact distribution of a lognormal sum has a lot of challenges, lognormal distributions are often used to approximate lognormal sum distributions.
J Almhana
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Pareto tails and lognormal body of US cities size distribution
Physica A: Statistical Mechanics and Its Applications, 2017 We consider a distribution, which consists of lower tail Pareto, lognormal body, and upper tail Pareto, to estimate the size distribution of all US cities.
Jeff Luckstead, Stephen Devadoss
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On Poisson Mixture of Lognormal Distributions
Lobachevskii Journal of Mathematics, 2020Generally, jump-diffusion processes used in finance are confined to the processes with Brownian motion, constant trend and jump component, described by compound Poisson processes (CPP). CPP is usually defined by a sum of standard normal distributions. In most applications one either needs moments or characteristic function of the process.
Kechejian, H. +2 more
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The College Mathematics Journal, 2000
(2000). The Lognormal Distribution. The College Mathematics Journal: Vol. 31, No. 4, pp. 259-261.
Brian E. Smith, Francis J. Merceret
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(2000). The Lognormal Distribution. The College Mathematics Journal: Vol. 31, No. 4, pp. 259-261.
Brian E. Smith, Francis J. Merceret
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Lognormal Distributions: Theory and Applications
Technometrics, 1989Lognormal Distributions: Theory and Applications (Statistics: Textbooks and Monographs Series, Vol. 88). Edited by E. L. Crow and K. Shimizu. ISBN 0 8247 7803 0. Dekker, New York, 1988. 408 pp. $79.75 (USA and Canada), $95.50 (elsewhere).
M. J. Fryer, E. L. Crow, K. Shimizu
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1999
As already mentioned in sections 4.3, 5.1, 9.1 and 12.7, the normal distribution is not always completely suitable in biology because many biological variates cannot take negative numerical values and have positively skewed frequency distributions (sections 13.2 and 13.7). The lognormal distribution is often better adapted to biological data. While the
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As already mentioned in sections 4.3, 5.1, 9.1 and 12.7, the normal distribution is not always completely suitable in biology because many biological variates cannot take negative numerical values and have positively skewed frequency distributions (sections 13.2 and 13.7). The lognormal distribution is often better adapted to biological data. While the
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Is Alcohol Consumption Lognormally Distributed?
British Journal of Addiction, 1980SummaryThe empirical evidence for the hypothesis of lognormality is reviewed. A theoretical, argument suggesting systematic deviations from lognormality is outlined, and some ‘new’ data are presented.
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Are electromigration failures lognormally distributed?
28th Annual Proceedings on Reliability Physics Symposium, 1990Electromigration lifetests performed on a variety of Al alloy films to study the form of the failure distribution are discussed. Sample sizes ranged from 35 to 120, allowing exploration near the 1% failure level. Results show that the lognormal rather than the logarithmic extreme value distribution holds where the grain size is smaller than the ...
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Journal of Hydrologic Engineering, 2002
The lognormal distribution is frequently used in hydrological studies. However, the distribution is in the form of an integral that cannot be expressed in the form of elementary functions. Thus, the lognormal distribution cannot be used for analytical purposes such as the application of the inverse transform method for generation of a sequence of ...
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The lognormal distribution is frequently used in hydrological studies. However, the distribution is in the form of an integral that cannot be expressed in the form of elementary functions. Thus, the lognormal distribution cannot be used for analytical purposes such as the application of the inverse transform method for generation of a sequence of ...
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Lognormal Distributions and Properties
1999If Y is normally distributed with mean μ and variance σ2, then the random variable X defined by the relationship Y =log(X - γ) is distributed as lognormal, and is denoted as lognormal(γ,μ,σ2).
N. Balakrishnan, William W. S. Chen
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