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Approximating the Sum of Correlated Lognormal or, Lognormal-Rice Random Variables
2006 IEEE International Conference on Communications, 2006A simple and novel method is presented to approximate by the lognormal distribution the probability density function of the sum of correlated lognormal random variables. The method is also shown to work well for approximating the distribution of the sum of lognormal-Rice or Suzuki random variables by the lognormal distribution.
Neelesh B. Mehta +3 more
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On the lognormality of radionuclide deposition
Journal of Environmental Radioactivity, 2015The influence of the variation of soil density and the uncertainty of activity measurements on the statistical distribution of radionuclide concentrations on a site is considered. It is demonstrated that the influence of these factors adequately explains the observed deviation of radionuclide empirical probability distribution functions (empirical PDFs)
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On the products of lognormal and cumulative lognormal particle size distributions
Journal of Aerosol Science, 1982Abstract The output distributions of particle size which would result when an input aerosol having a lognormal size distribution is passed through a size classifier (e.g. impactor) or control device (e.g. cyclone) having a cumulative lognormal penetration function are investigated theoretically.
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Journal of Applied Probability, 1981
Models have been proposed in many diverse areas to generate a lognormal distribution and the underlying idea has always been some form of the law of proportionate effect. In a sense any model must resemble this recipe: take logs and apply the central limit theorem. Our model is no exception. However our formulation is designed to encompass the previous
Brown, Gavin, Sanders, J. W.
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Models have been proposed in many diverse areas to generate a lognormal distribution and the underlying idea has always been some form of the law of proportionate effect. In a sense any model must resemble this recipe: take logs and apply the central limit theorem. Our model is no exception. However our formulation is designed to encompass the previous
Brown, Gavin, Sanders, J. W.
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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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