|Probability density function
Some log-normal density functions with identical location parameter μ but differing scale parameters σ
|Cumulative distribution function
Cumulative distribution function of the log-normal distribution (with μ = 0 )
|Parameters||σ2 > 0 — shape (real),
μ ∈ R — log-scale
|Support||x ∈ (0, +∞)|
|MGF||(defined only on the negative half-axis, see text)|
|CF||representation is asymptotically divergent but sufficient for numerical purposes|
In probability theory, a log-normal distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable Y is log-normally distributed, then X = log(Y) has a normal distribution. Likewise, if X has a normal distribution, then Y = exp(X) has a log-normal distribution. A random variable which is log-normally distributed takes only positive real values.
Log-normal is also written log normal or lognormal. The distribution is occasionally referred to as the Galton distribution or Galton's distribution, after Francis Galton. The log-normal distribution also has been associated with other names, such as McAlister, Gibrat and Cobb–Douglas.
A variable might be modeled as log-normal if it can be thought of as the multiplicative product of many independent random variables each of which is positive. (This is justified by considering the central limit theorem in the log-domain.) For example, in finance, the variable could represent the compound return from a sequence of many trades (each expressed as its return + 1); or a long-term discount factor can be derived from the product of short-term discount factors. In wireless communication, the sas caused by shadowing or slow fading from random objects is often assumed to be log-normally distributed: see log-distance path loss model.
μ and σ 
In a log-normal distribution X, the parameters denoted μ and σ are, respectively, the mean and standard deviation of the variable’s natural logarithm (by definition, the variable’s logarithm is normally distributed), which means
with Z a standard normal variable.
This relationship is true regardless of the base of the logarithmic or exponential function. If loga(Y) is normally distributed, then so is logb(Y), for any two positive numbers a, b ≠ 1. Likewise, if is normally distributed, then so is , where a is a positive number ≠ 1.
On a logarithmic scale, μ and σ can be called the location parameter and the scale parameter, respectively.
In contrast, the mean and standard deviation of the non-logarithmized sample values are denoted m and s.d. in this article.
Probability density function 
This follows by applying the change-of-variables rule on the density function of a normal distribution.
Cumulative distribution function 
Characteristic function and moment generating function 
The characteristic function, E[e itX], has a number of representations. The integral itself converges for Im(t) ≤ 0. The simplest representation is obtained by Taylor expanding e itX and using formula for moments below, giving
This series representation is divergent for Re(σ2) > 0. However, it is sufficient for evaluating the characteristic function numerically at positive as long as the upper limit in the sum above is kept bounded, n ≤ N, where
and σ2 < 0.1. To bring the numerical values of parameters μ, σ into the domain where strong inequality holds true one could use the fact that if X is log-normally distributed then Xm is also log-normally distributed with parameters μm, σm. Since , the inequality could be satisfied for sufficiently small m. The sum of series first converges to the value of φ(t) with arbitrary high accuracy if m is small enough, and left part of the strong inequality is satisfied. If considerably larger number of terms are taken into account the sum eventually diverges when the right part of the strong inequality is no longer valid.
Location and scale 
For the log-normal distribution, the location and scale properties of the distribution are more readily treated using the geometric mean and geometric standard deviation than the arithmetic mean and standard deviation.
Geometric moments 
The geometric mean of the log-normal distribution is . Because the log of a log-normal variable is symmetric and quantiles are preserved under monotonic transformations, the geometric mean of a log-normal distribution is equal to its median.
The geometric mean (mg) can alternatively be derived from the arithmetic mean (ma) in a log-normal distribution by:
Note that the geometric mean is less than the arithmetic mean. This is due to the AM–GM inequality, and corresponds to the logarithm being convex down. The correction term can accordingly be interpreted as a convexity correction. From the point of view of stochastic calculus, this is the same correction term as in Itō's lemma for geometric Brownian motion.
Arithmetic moments 
Equivalently, parameters μ and σ can be obtained if the expected value and variance are known; it is simpler if σ is computed first:
A log-normal distribution is not uniquely determined by its moments E[Xk] for k ≥ 1, that is, there exists some other distribution with the same moments for all k. In fact, there is a whole family of distributions with the same moments as the log-normal distribution.
Mode and median 
The mode is the point of global maximum of the probability density function. In particular, it solves the equation (ln ƒ)′ = 0:
The median is such a point where FX = 1/2:
Coefficient of variation 
The coefficient of variation is the ratio s.d. over m (on the natural scale) and is equal to:
Partial expectation 
The partial expectation of a random variable X with respect to a threshold k is defined as where is the probability density function of X. Alternatively, and using the definition of conditional expectation, it can be written as g(k)=E[X | X > k]*P(X > k). For a log-normal random variable the partial expectation is given by:
The derivation of the formula is provided in the discussion of this Wikipedia entry. The partial expectation formula has applications in insurance and economics, it is used in solving the partial differential equation leading to the Black–Scholes formula.
The harmonic (H), geometric (G) and arithmetic (A) means of this distribution are related; such relation is given by
- In biology, variables whose logarithms tend to have a normal distribution include:
- Measures of size of living tissue (length, skin area, weight);
- The length of inert appendages (hair, claws, nails, teeth) of biological specimens, in the direction of growth;
- Certain physiological measurements, such as blood pressure of adult humans (after separation on male/female subpopulations)
- Consequently, reference ranges for measurements in healthy individuals are more accurately estimated by assuming a log-normal distribution than by assuming a symmetric distribution about the mean.
- In hydrology, the log-normal distribution is used to analyze extreme values of such variables as monthly and annual maximum values of daily rainfall and river discharge volumes.
- The image on the right illustrates an example of fitting the log-normal distribution to ranked annually maximum one-day rainfalls showing also the 90% confidence belt based on the binomial distribution. The rainfall data are represented by plotting positions as part of a cumulative frequency analysis.
- In economics, there is evidence that the income of 97%–99% of the population is distributed log-normally.
- In finance, in particular the Black–Scholes model, changes in the logarithm of exchange rates, price indices, and stock market indices are assumed normal (these variables behave like compound interest, not like simple interest, and so are multiplicative). However, some mathematicians such as Benoît Mandelbrot have argued  that log-Lévy distributions which possesses heavy tails would be a more appropriate model, in particular for the analysis for stock market crashes. Indeed stock price distributions typically exhibit a fat tail.
- The distribution of city sizes is lognormal. This follows from Gibrat's law of proportionate (or scale-free) growth. Irrespective of their size, all cities follow the same stochastic growth process. As a result, the logarithm of city size is normally distributed. There is also evidence of lognormality in the firm size distribution and of Gibrat's law.
- In reliability analysis, the lognormal distribution is often used to model times to repair a maintainable system.
- In wireless communication, "the local-mean power expressed in logarithmic values, such as dB or neper, has a normal (i.e., Gaussian) distribution." 
- It has been proposed that coefficients of friction and wear may be treated as having a lognormal distribution 
Maximum likelihood estimation of parameters 
where by ƒL we denote the probability density function of the log-normal distribution and by ƒN 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 regard to μ and σ, both logarithmic likelihood functions, ℓL and ℓN, 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
Multivariate log-normal 
Generating log-normally distributed random variates 
Given a random variate Z drawn from the normal distribution with 0 mean and 1 standard deviation, then the variate
has a log-normal distribution with parameters and .
Related distributions 
- If is a normal distribution, then
- If is distributed log-normally, then is a normal random variable.
- If are n independent log-normally distributed variables, and , then Y is also distributed log-normally:
- Let be independent log-normally distributed variables with possibly varying σ and μ parameters, and . The distribution of Y has no closed-form expression, but can be reasonably approximated by another log-normal distribution Z at the right tail. Its probability density function at the neighborhood of 0 has been characterized and it does not resemble any log-normal distribution. A commonly used approximation (due to L.F. Fenton, but previously stated by R.I. Wilkinson without mathematical justification) is obtained by matching the mean and variance:
In the case that all have the same variance parameter , these formulas simplify to
- If , then X + c is said to have a shifted log-normal distribution with support x ∈ (c, +∞). E[X + c] = E[X] + c, Var[X + c] = Var[X].
- If , then
- If , then
- If then for
- Lognormal distribution is a special case of semi-bounded Johnson distribution
- If with , then (Suzuki distribution)
Similar distributions 
This is a log-logistic distribution.
See also 
- Johnson, Norman L.; Kotz, Samuel; Balakrishnan, N. (1994), "14: Lognormal Distributions", Continuous univariate distributions. Vol. 1, Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics (2nd ed.), New York: John Wiley & Sons, ISBN 978-0-471-58495-7, MR 1299979
- Park, Sung Y.; Bera, Anil K. (2009). "Maximum entropy autoregressive conditional heteroskedasticity model". Journal of Econometrics (Elsevier): 219–230. Retrieved 2011-06-02.
- Leipnik, Roy B. (1991), "On Lognormal Random Variables: I – The Characteristic Function", Journal of the Australian Mathematical Society Series B, 32, 327–347.
- Daniel Dufresne (2009), SUMS OF LOGNORMALS, Centre for Actuarial Studies, University of Melbourne.
- Leslie E. Daly, Geoffrey Joseph Bourke (2000) Interpretation and uses of medical statistics Edition: 5. Wiley-Blackwell ISBN 0-632-04763-1, ISBN 978-0-632-04763-5 (page 89)
- Damgaard, Christian; Weiner, Jacob (2000). "Describing inequality in plant size or fecundity". Ecology 81 (4): 1139–1142. doi:10.1890/0012-9658(2000)081[1139:DIIPSO]2.0.CO;2.
- Rossman LA (1990) "Design stream ﬂows based on harmonic means". J Hydraulic Engineering ASCE 116 (7) 946–950
- Huxley, Julian S. (1932). Problems of relative growth. London. ISBN 0-486-61114-0. OCLC 476909537.
- Makuch, Robert W.; D.H. Freeman, M.F. Johnson (1979). "Justification for the lognormal distribution as a model for blood pressure". Journal of Chronic Diseases 32 (3): 245–250. doi:10.1016/0021-9681(79)90070-5. (http://www.sciencedirect.com/science/article/pii/0021968179900705. Retrieved 27 February 2012.
- Ritzema (ed.), H.P. (1994). Frequency and Regression Analysis. Chapter 6 in: Drainage Principles and Applications, Publication 16, International Institute for Land Reclamation and Improvement (ILRI), Wageningen, The Netherlands. pp. 175–224. ISBN 90-70754-33-9.
- Clementi, F.; Gallegati, M. (2005) "Pareto's law of income distribution: Evidence for Germany, the United Kingdom, and the United States", EconWPA
- Black, Fischer and Myron Scholes, "The Pricing of Options and Corporate Liabilities", Journal of Political Economy, Vol. 81, No. 3, (May/June 1973), pp. 637–654.
- Madelbrot, Beniot (2004). The (mis-)Behaviour of Markets.
- Bunchen, P., Advanced Option Pricing, University of Sydney coursebook, 2007
- http://wireless.per.nl/reference/chaptr03/shadow/shadow.htm[dead link]
- Steele, C. (2008). "Use of the lognormal distribution for the coefficients of friction and wear". Reliability Engineering & System Safety 93 (10): 1574–2013. doi:10.1016/j.ress.2007.09.005.
- Tarmast, Ghasem (2001) "Multivariate Log–Normal Distribution" ISI Proceedings: Seoul 53rd Session 2001
- Gao, X.; Xu, H; Ye, D. (2009), "Asymptotic Behaviors of Tail Density for Sum of Correlated Lognormal Variables". International Journal of Mathematics and Mathematical Sciences, vol. 2009, Article ID 630857. doi:10.1155/2009/630857
- Daniel Dufresne (2009), SUMS OF LOGNORMALS, Centre for Actuarial Studies, University of Melbourne.
- Swamee, P. K. (2002). "Near Lognormal Distribution". Journal of Hydrologic Engineering 7 (6): 441–444. doi:10.1061/(ASCE)1084-0699(2002)7:6(441).
- Aitchison, J. and Brown, J.A.C. (1957) The Lognormal Distribution, Cambridge University Press.
- E. Limpert, W. Stahel and M. Abbt (2001) Log-normal Distributions across the Sciences: Keys and Clues, BioScience, 51 (5), 341–352.
- Eric W. Weisstein et al. Log Normal Distribution at MathWorld. Electronic document, retrieved October 26, 2006.
- Holgate, P. (1989). "The lognormal characteristic function". Communications in Statistics - Theory and Methods 18 (12): 4539–4548. doi:10.1080/03610928908830173.
Further reading 
- 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.
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