Lognormal Distributions: Theory and Applications.
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Lognormal 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).Keywords:
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For national or global resource estimation of frequencies of metals,a lognormal distribution has commonly been recommended but not adequately tested.Tests of frequencies of Cu,Zn,Pb,Ag,and Au contents of 1 984 well-explored mineral deposits display a poor fit to the lognormal distribution.When the same metals plus Mo,Co,Nb2O3,and REE2O3 are grouped into 19 geologically defined deposit types,only eight of the 73 tests fail to be fit by lognormal distribution,and most of those failures are in two deposit types suggesting a problem with those types.Estimates of the mean and standard deviation of each of the metals in each of the deposit types are provided for modeling.
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Journal of the Chemical Society Faraday Transactions 1 Physical Chemistry in Condensed Phases (1980)
Equations are derived for the reaction kinetics of powders in which a particle of size D reacts according to dFD/d(tz)=(kz/βD)(1 –βF)m and which have lognormal, truncated lognormal or composite lognormal particle size distributions. This leads to a method for estimating such kinetics for powders with arbitrary particle size distribution.For β= 1 and either linear kinetics (z= 1, m=⅔) or parabolic kinetics (z=½, m= 0.43) lognormal powders with 0.2 < σ < 1.2 will approximately obey the same equation with median size for D and with m and ln k approximately linear functions of σ.
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Particle (ecology)
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The thesis introduce a solution of using DataGrid coned and ComboBox control to realize DataBase of querying
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qlognorm plots the quantiles of varname against the quantiles of the corresponding lognormal distribution. plognorm graphs a standardized lognormal probability plot for varname. The (two-parameter) lognormal distribution fitted corresponds to a normal distribution with the mean and standard deviation of log(varname).
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Abstract Geometric means are often more meaningful than arithmetic means, because they are closer to the central figure (median). When x and y can conceptually take only positive values, their distributions cannot be normal and may be lognormal. When running a normal distribution yields a standard deviation larger than one-half of the mean, one should dump the normal fit and try a lognormal fit instead.
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In this paper, accelerated life tests of white organic light-emitting diodes (WOLEDs) are conducted to obtain failure data at normal operation conditions. The lognormal distribution function was applied to describe WOLED life distribution. Log mean and log standard deviation were determined by maximum likelihood estimation. The Kolmogorov-Smirnov test was performed, and the results further confirmed that WOLED life met the lognormal distribution. Numerical results indicated that WOLED life followed the lognormal distribution. It was also found that the acceleration model was consistent with inverse power law.
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This paper analyzed the DataGrid Web Server Control. DataGrid is one of the most popular control of ASP .NET which is used to render data to a Web page in tabular form. This paper provides two types of typical usage of DataGrid.
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This paper theoretically analyzes a phenomenological stochastic model for bacterial growth. This model comprises cell division and the linear growth of cells, where growth rates and cell cycles are drawn from lognormal distributions. We find that the cell size is expressed as a sum of independent lognormal variables. We show numerically that the quality of the lognormal approximation greatly depends on the distributions of the growth rate and cell cycle. Furthermore, we show that actual parameters of the growth rate and cell cycle take values that give a good lognormal approximation; thus, the experimental cell-size distribution is in good agreement with a lognormal distribution.
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In 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. However, it has been shown that lognormal approximations can only capture a certain part of the body of a lognormal sum distribution, which implies that to accurately approximate a lognormal sum distribution, one has to resort to non-lognormal approximations. In this paper we propose to use a two-component mixture lognormal model to approximate lognormal sum distributions. Numerical examples are provided to compare the proposed mixture lognormal approximation with the existing ones.
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