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In probability and statistics, the log-normal distribution is the probability distribution of any random variable whose logarithm is normally distributed. If X is a random variable with a normal distribution, then exp(X) has a log-normal distribution; likewise, if Y is log-normally distributed, then log(Y) is normally distributed.
"Log-normal" is also written "log normal", "lognormal" or "logistic normal".
A variable might be modeled as log-normal if it can be thought of as the multiplicative product of many small independent factors. A typical example is the long-term return rate on a stock investment: it can be considered as the product of the daily return rates.
The log-normal distribution has probability density function (pdf)
and the variance is
Equivalent relationships may be written to obtain and given the expected value and standard deviation:
Relationship to geometric mean and geometric standard deviationEdit
If a sample of data is determined to come from a log-normally distributed population, the geometric mean and the geometric standard deviation may be used to estimate confidence intervals akin to the way the arithmetic mean and standard deviation are used to estimate confidence intervals for a normally distributed sample of data.
|Confidence interval bounds||log space||geometric|
|3σ lower bound|
|2σ lower bound|
|1σ lower bound|
|1σ upper bound|
|2σ upper bound|
|3σ upper bound|
Where geometric mean and geometric standard deviation
The first few raw moments are:
Partial expectation Edit
The partial expectation of a random variable with respect to a threshold is defined as
where is the density. For a lognormal density it can be shown that
where is the cumulative distribution function of the standard normal. The partial expectation of a lognormal has applications in insurance and in economics.
Maximum likelihood estimation of parameters Edit
where by we denote the density probability function of the log-normal distribution and by —that of the normal distribution. Therefore, using the same indices to denote distributions, we can write the log-likelihood function thus:
Since the first term is constant with regards to μ and σ, both logarithmic likelihood functions, and , reach their maximum with the same μ and σ. Hence, using the formulas for the normal distribution maximum likelihood parameter estimators and the equality above, we deduce that for the log-normal distribution it holds that
- is a normal distribution if and .
- If are independent log-normally distributed variables with the same μ parameter and possibly varying σ, and , then Y is a log-normally distributed variable as well: .
- Robert Brooks, Jon Corson, and J. Donal Wales. "The Pricing of Index Options When the Underlying Assets All Follow a Lognormal Diffusion", in Advances in Futures and Options Research, volume 7, 1994.
- The Lognormal Distribution, Aitchison, J. and Brown, J.A.C. (1957)
- Log-normal Distributions across the Sciences: Keys and Clues, E. Limpert, W. Stahel and M. Abbt,. BioScience, 51 (5), p. 341–352 (2001).
- Properties of Lognormal Distribution, John Hull, in Options, Futures, and Other Derivatives 6E (2005). ISBN 0-13-149908-4
- Eric W. Weisstein et al. Log Normal Distribution at MathWorld. Electronic document, retrieved October 26, 2006.
See alsoEditde:Logarithmische Normalverteilung
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