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Probability density function
Lognormal distribution PDF
Cumulative distribution function
Lognormal distribution CDF
Parameters \sigma \ge 0
-\infty \le \mu \le \infty
Support x \in [0; +\infty)\!
pdf \exp\left(-\left.\left[\frac{\ln(x)-\mu}{\sigma}\right]^2\right/2\right) \left/ \left(x\sigma\sqrt{2\pi}\right) \right.
cdf \frac{1}{2}+\frac{1}{2} \mathrm{erf}\left[\frac{\ln(x)-\mu}{\sigma\sqrt{2}}\right]
Mean e^{\mu+\sigma^2/2}
Median e^{\mu}
Mode e^{\mu-\sigma^2}
Variance (e^{\sigma^2}\!\!-1) e^{2\mu+\sigma^2}
Skewness (e^{\sigma^2}\!\!+2)\sqrt{e^{\sigma^2}\!\!-1}
Kurtosis e^{4\sigma^2}\!\!+2e^{3\sigma^2}\!\!+3e^{2\sigma^2}\!\!-6
Entropy \frac{1}{2}+\frac{1}{2}\ln(2\pi\sigma^2) + \mu
mgf (see text for raw moments)
Char. func.

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)

f(x;\mu,\sigma) = \frac{1}{x \sigma \sqrt{2 \pi}} e^{-(\ln x - \mu)^2/2\sigma^2}

for x>0, where \mu and \sigma are the mean and standard deviation of the variable's logarithm. The expected value is

\mathrm{E}(X) = e^{\mu + \sigma^2/2}

and the variance is

\mathrm{var}(X) = (e^{\sigma^2} - 1) e^{2\mu + \sigma^2}.\,

Equivalent relationships may be written to obtain \mu and \sigma given the expected value and standard deviation:

\mu = \ln(\mathrm{E}(X))-\frac{1}{2}\ln\left(1+\frac{\mathrm{var}(X)}{\mathrm{E}(X)^2}\right),
\sigma^2 = \ln\left(1+\frac{\mathrm{var}(X)}{\mathrm{E}(X)^2}\right).

Relationship to geometric mean and geometric standard deviationEdit

Log-normal distribution the geometric mean, and the geometric standard deviation are related. In this case, the geometric mean is equal to \exp(\mu) and the geometric standard deviation is equal to \exp(\sigma).

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 \mu - 3\sigma \mu_\mathrm{geo} / \sigma_\mathrm{geo}^3
2σ lower bound \mu - 2\sigma \mu_\mathrm{geo} / \sigma_\mathrm{geo}^2
1σ lower bound \mu - \sigma \mu_\mathrm{geo} / \sigma_\mathrm{geo}
1σ upper bound \mu + \sigma \mu_\mathrm{geo} \sigma_\mathrm{geo}
2σ upper bound \mu + 2\sigma \mu_\mathrm{geo} \sigma_\mathrm{geo}^2
3σ upper bound \mu + 3\sigma \mu_\mathrm{geo} \sigma_\mathrm{geo}^3

Where geometric mean \mu_\mathrm{geo} = \exp(\mu) and geometric standard deviation \sigma_\mathrm{geo} = \exp(\sigma)

Moments Edit

The first few raw moments are:


or generally:


Partial expectation Edit

The partial expectation of a random variable X with respect to a threshold k is defined as

g(k)=\int_k^\infty (x-k) f(x)\, dx

where f(x) is the density. For a lognormal density it can be shown that

g(k)=\exp(\mu+\sigma^2/2)\Phi\left(\frac{-\ln(k)+\mu+\sigma^2}{\sigma}\right)-k \Phi\left(\frac{-\ln(k)+\mu}{\sigma}\right)

where \Phi 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

For determining the maximum likelihood estimators of the log-normal distribution parameters μ and σ, we can use the same procedure as for the normal distribution. To avoid repetition, we observe that

f_L (x;\mu, \sigma) = \frac 1 x \, f_N (\ln x; \mu, \sigma)

where by f_L (\cdot) we denote the density probability function of the log-normal distribution and by f_N (\cdot)—that of the normal distribution. Therefore, using the same indices to denote distributions, we can write the log-likelihood function thus:

  \ell_L (\mu,\sigma | x_1, x_2, ..., x_n)
  & = & - \sum _k \ln x_k + \ell_N (\mu, \sigma | \ln x_1, \ln x_2, \dots, \ln x_n) = \\  \\
\ & = & \operatorname {constant} + \ell_N (\mu, \sigma | \ln x_1, \ln x_2, \dots, \ln x_n).

Since the first term is constant with regards to μ and σ, both logarithmic likelihood functions, \ell_L and \ell_N, 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

\widehat \mu = \frac {\sum_k \ln x_k} n, \ 
        \widehat \sigma^2 = \frac {\sum_k {\left( \ln x_k - \widehat \mu \right)^2}} n.

Related distributionsEdit

  • Y \sim N(\mu, \sigma^2) is a normal distribution if Y = \ln(X) and X \sim \operatorname{Log-N}(\mu, \sigma^2).
  • If X_m \sim \operatorname {Log-N} (\mu, \sigma_m^2), \ m = \overline {1 ... n} are independent log-normally distributed variables with the same μ parameter and possibly varying σ, and Y = \prod_{m=1}^n X_m, then Y is a log-normally distributed variable as well: Y \sim \operatorname {Log-N} \left( n\mu, \sum _{m=1}^n \sigma_m^2 \right).

Further readingEdit


See alsoEdit

Bvn-small Probability distributions [[[:Template:Tnavbar-plain-nodiv]]]
Univariate Multivariate
Discrete: BernoullibinomialBoltzmanncompound PoissondegeneratedegreeGauss-Kuzmingeometrichypergeometriclogarithmicnegative binomialparabolic fractalPoissonRademacherSkellamuniformYule-SimonzetaZipfZipf-Mandelbrot Ewensmultinomial
Continuous: BetaBeta primeCauchychi-squareDirac delta functionErlangexponentialexponential powerFfadingFisher's zFisher-TippettGammageneralized extreme valuegeneralized hyperbolicgeneralized inverse GaussianHotelling's T-squarehyperbolic secanthyper-exponentialhypoexponentialinverse chi-squareinverse gaussianinverse gammaKumaraswamyLandauLaplaceLévyLévy skew alpha-stablelogisticlog-normalMaxwell-BoltzmannMaxwell speednormal (Gaussian)ParetoPearsonpolarraised cosineRayleighrelativistic Breit-WignerRiceStudent's ttriangulartype-1 Gumbeltype-2 GumbeluniformVoigtvon MisesWeibullWigner semicircle DirichletKentmatrix normalmultivariate normalvon Mises-FisherWigner quasiWishart
Miscellaneous: Cantorconditionalexponential familyinfinitely divisiblelocation-scale familymarginalmaximum entropy phase-typeposterior priorquasisampling
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