Weibull distribution

Weibull (2-parameter)
	Probability density function
	Cumulative distribution function
Parameters	scale ; shape
Support
PDF
CDF
Mean
Median
Mode
Variance
Skewness
Ex. kurtosis	(see text)
Entropy
MGF
CF

In probability theory and statistics, the Weibull distribution /ˈveɪbʊl/ is a continuous probability distribution. It is named after Swedish mathematician Waloddi Weibull, who described it in detail in 1951, although it was first identified by Fréchet (1927) and first applied by Rosin & Rammler (1933) to describe a particle size distribution.

Definition

Standard parameterization

The probability density function of a Weibull random variable is:^[1]

f(x;\lambda,k) = \begin{cases} \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1}e^{-(x/\lambda)^{k}} & x\geq0 ,\\ 0 & x<0, \end{cases}

where k > 0 is the shape parameter and λ > 0 is the scale parameter of the distribution. Its complementary cumulative distribution function is a stretched exponential function. The Weibull distribution is related to a number of other probability distributions; in particular, it interpolates between the exponential distribution (k = 1) and the Rayleigh distribution (k = 2 and $\lambda = \sqrt{2}\sigma$ ^[2]).

If the quantity X is a "time-to-failure", the Weibull distribution gives a distribution for which the failure rate is proportional to a power of time. The shape parameter, k, is that power plus one, and so this parameter can be interpreted directly as follows:^[3]

A value of $k<1\,$ indicates that the failure rate decreases over time (Lindy effect). This happens if there is significant "infant mortality", or defective items failing early and the failure rate decreasing over time as the defective items are weeded out of the population. In the context of the diffusion of innovations, this means negative word of mouth: the hazard function is a monotonically decreasing function of the proportion of adopters;
A value of $k=1\,$ indicates that the failure rate is constant over time. This might suggest random external events are causing mortality, or failure. The Weibull distribution reduces to an exponential distribution;
A value of $k>1\,$ indicates that the failure rate increases with time. This happens if there is an "aging" process, or parts that are more likely to fail as time goes on. In the context of the diffusion of innovations, this means positive word of mouth: the hazard function is a monotonically increasing function of the proportion of adopters. The function is first convex, then concave with an inflexion point at $(e^{1/k}-1)/e^{1/k},k>1\,$ .

In the field of materials science, the shape parameter k of a distribution of strengths is known as the Weibull modulus. In the context of diffusion of innovations, the Weibull distribution is a "pure" imitation/rejection model.

Alternative parameterizations

In medical statistics a different parameterization is used.^[4] The shape parameter k is the same as above and the scale parameter is $b=\lambda ^{-k}$ . For x ≥ 0 the hazard function is

h(x;k,b)=bkx^{k-1},

and the probability density function is

f(x;k,b)=bkx^{k-1}\exp(-bx^{k}).

The mean is

b^{-1/k}\Gamma (1+1/k).

A third parameterization is sometimes used. In this the shape parameter k is the same as above and the scale parameter is $\beta =1/\lambda$ .

Properties

Density function

The form of the density function of the Weibull distribution changes drastically with the value of k. For 0 < k < 1, the density function tends to ∞ as x approaches zero from above and is strictly decreasing. For k = 1, the density function tends to 1/λ as x approaches zero from above and is strictly decreasing. For k > 1, the density function tends to zero as x approaches zero from above, increases until its mode and decreases after it. The density function has infinite negative slope at x = 0 if 0 < k < 1, infinite positive slope at x = 0 if 1 < k < 2 and null slope at x = 0 if k > 2. For k = 2 the density has a finite positive slope at x = 0. As k goes to infinity, the Weibull distribution converges to a Dirac delta distribution centered at x = λ. Moreover, the skewness and coefficient of variation depend only on the shape parameter.

Cumulative distribution function

The cumulative distribution function for the Weibull distribution is

F(x;k,\lambda) = 1- e^{-(x/\lambda)^k}\,

for x ≥ 0, and F(x; k; λ) = 0 for x < 0.

If x = λ then F(x; k; λ) = $1-e^{-1}\,$ ≈ 0.632 for all values of k. Vice versa: at F(x; k; λ) = 0.632 the value of x ≈ λ.

The quantile (inverse cumulative distribution) function for the Weibull distribution is

Q(p;k,\lambda) = \lambda {(-\ln(1-p))}^{1/k}

≈ 0.632 for all values of k.

for 0 ≤ p < 1.

The failure rate h (or hazard function) is given by

h(x;k,\lambda) = {k \over \lambda} \left({x \over \lambda}\right)^{k-1}.

Moments

The moment generating function of the logarithm of a Weibull distributed random variable is given by^[5]

\operatorname {E} \left[e^{t\log X}\right]=\lambda ^{t}\Gamma \left({\frac {t}{k}}+1\right)

where $Γ$ is the gamma function. Similarly, the characteristic function of log X is given by

\operatorname {E} \left[e^{it\log X}\right]=\lambda ^{it}\Gamma \left({\frac {it}{k}}+1\right).

In particular, the nth raw moment of X is given by

m_n = \lambda^n \Gamma\left(1+\frac{n}{k}\right).

The mean and variance of a Weibull random variable can be expressed as

\operatorname {E} (X)=\lambda \Gamma \left(1+{\frac {1}{k}}\right)\,

and

\operatorname {var} (X)=\lambda ^{2}\left[\Gamma \left(1+{\frac {2}{k}}\right)-\left(\Gamma \left(1+{\frac {1}{k}}\right)\right)^{2}\right]\,.

The skewness is given by

\gamma_1=\frac{\Gamma\left(1+\frac{3}{k}\right)\lambda^3-3\mu\sigma^2-\mu^3}{\sigma^3}

where the mean is denoted by $μ$ and the standard deviation is denoted by $σ$ .

The excess kurtosis is given by

\gamma _{2}={\frac {-6\Gamma _{1}^{4}+12\Gamma _{1}^{2}\Gamma _{2}-3\Gamma _{2}^{2}-4\Gamma _{1}\Gamma _{3}+\Gamma _{4}}{[\Gamma _{2}-\Gamma _{1}^{2}]^{2}}}

where $\Gamma_i=\Gamma(1+i/k)$ . The kurtosis excess may also be written as:

\gamma_{2}=\frac{\lambda^4\Gamma(1+\frac{4}{k})-4\gamma_{1}\sigma^3\mu-6\mu^2\sigma^2-\mu^4}{\sigma^4}-3

Moment generating function

A variety of expressions are available for the moment generating function of X itself. As a power series, since the raw moments are already known, one has

\operatorname {E} \left[e^{tX}\right]=\sum _{n=0}^{\infty }{\frac {t^{n}\lambda ^{n}}{n!}}\Gamma \left(1+{\frac {n}{k}}\right).

Alternatively, one can attempt to deal directly with the integral

\operatorname {E} \left[e^{tX}\right]=\int _{0}^{\infty }e^{tx}{\frac {k}{\lambda }}\left({\frac {x}{\lambda }}\right)^{k-1}e^{-(x/\lambda )^{k}}\,dx.

If the parameter k is assumed to be a rational number, expressed as k = p/q where p and q are integers, then this integral can be evaluated analytically.^[6] With t replaced by −t, one finds

\operatorname {E} \left[e^{-tX}\right]={\frac {1}{\lambda ^{k}\,t^{k}}}\,{\frac {p^{k}\,{\sqrt {q/p}}}{({\sqrt {2\pi }})^{q+p-2}}}\,G_{p,q}^{\,q,p}\!\left(\left.{\begin{matrix}{\frac {1-k}{p}},{\frac {2-k}{p}},\dots ,{\frac {p-k}{p}}\\{\frac {0}{q}},{\frac {1}{q}},\dots ,{\frac {q-1}{q}}\end{matrix}}\;\right|\,{\frac {p^{p}}{\left(q\,\lambda ^{k}\,t^{k}\right)^{q}}}\right)

where G is the Meijer G-function.

The characteristic function has also been obtained by Muraleedharan et al. (2007). The characteristic function and moment generating function of 3-parameter Weibull distribution have also been derived by Muraleedharan & Soares (2014) by a direct approach.

Shannon entropy

The information entropy is given by

H(\lambda,k) = \gamma\left(1\!-\!\frac{1}{k}\right) + \ln\left(\frac{\lambda}{k}\right) + 1

where $\gamma$ is the Euler–Mascheroni constant. The Weibull distribution is the maximum entropy distribution for a non-negative real random variate with a fixed expected value of x^k equal to λ^k and a fixed expected value of ln(x^k ) equal to ln(λ^k ) - $\gamma$ .

Parameter estimation

Maximum likelihood

The maximum likelihood estimator for the $\lambda$ parameter given $k$ is

{\widehat {\lambda }}^{k}={\frac {1}{n}}\sum _{i=1}^{n}x_{i}^{k}

The maximum likelihood estimator for $k$ is the solution for k of the following equation

0={\frac {\sum _{i=1}^{n}x_{i}^{k}\ln x_{i}}{\sum _{i=1}^{n}x_{i}^{k}}}-{\frac {1}{k}}-{\frac {1}{n}}\sum _{i=1}^{n}\ln x_{i}

This equation defining ${\widehat {k}}$ only implicitly, one must generally solve for $k$ by numerical means.

When $x_{1}>x_{2}>\cdots >x_{N}$ are the $N$ largest observed samples from a dataset of more than $N$ samples, then the maximum likelihood estimator for the $\lambda$ parameter given $k$ is^[7]

{\widehat {\lambda }}^{k}={\frac {1}{N}}\sum _{i=1}^{N}(x_{i}^{k}-x_{N}^{k})

Also given that condition, the maximum likelihood estimator for $k$ is

0={\frac {\sum _{i=1}^{N}(x_{i}^{k}\ln x_{i}-x_{N}^{k}\ln x_{N})}{\sum _{i=1}^{N}(x_{i}^{k}-x_{N}^{k})}}-{\frac {1}{N}}\sum _{i=1}^{N}\ln x_{i}

Again, this being an implicit function, one must generally solve for $k$ by numerical means.

Weibull plot

The fit of data to a Weibull distribution can be visually assessed using a Weibull plot.^[8] The Weibull plot is a plot of the empirical cumulative distribution function ${\widehat {F}}(x)$ of data on special axes in a type of Q-Q plot. The axes are $\ln(-\ln(1-{\widehat {F}}(x)))$ versus $\ln(x)$ . The reason for this change of variables is the cumulative distribution function can be linearized:

{\begin{aligned}F(x)&=1-e^{-(x/\lambda )^{k}}\\[4pt]-\ln(1-F(x))&=(x/\lambda )^{k}\\[4pt]\underbrace {\ln(-\ln(1-F(x)))} _{\textrm {'y'}}&=\underbrace {k\ln x} _{\textrm {'mx'}}-\underbrace {k\ln \lambda } _{\textrm {'c'}}\end{aligned}}

which can be seen to be in the standard form of a straight line. Therefore, if the data came from a Weibull distribution then a straight line is expected on a Weibull plot.

There are various approaches to obtaining the empirical distribution function from data: one method is to obtain the vertical coordinate for each point using ${\widehat {F}}={\frac {i-0.3}{n+0.4}}$ where $i$ is the rank of the data point and $n$ is the number of data points.^[9]

Linear regression can also be used to numerically assess goodness of fit and estimate the parameters of the Weibull distribution. The gradient informs one directly about the shape parameter $k$ and the scale parameter $\lambda$ can also be inferred.

Applications

The Weibull distribution is used

In survival analysis^[10]
In reliability engineering and failure analysis
In electrical engineering to represent overvoltage occurring in an electrical system
In industrial engineering to represent manufacturing and delivery times
In extreme value theory
In weather forecasting and the wind power industry to describe wind speed distributions, as the natural distribution often matches the Weibull shape^[11]
In communications systems engineering
- In radar systems to model the dispersion of the received signals level produced by some types of clutters
- To model fading channels in wireless communications, as the Weibull fading model seems to exhibit good fit to experimental fading channel measurements

Fitted cumulative Weibull distribution to maximum one-day rainfalls using CumFreq, see also distribution fitting ^[12]

In information retrieval to model dwell times on web pages^[13].

In general insurance to model the size of reinsurance claims, and the cumulative development of asbestosis losses
In forecasting technological change (also known as the Sharif-Islam model)^[14]
In hydrology the Weibull distribution is applied to extreme events such as annual maximum one-day rainfalls and river discharges. The blue picture illustrates an example of fitting the Weibull 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 the cumulative frequency analysis.
In describing the size of particles generated by grinding, milling and crushing operations, the 2-Parameter Weibull distribution is used, and in these applications it is sometimes known as the Rosin-Rammler distribution. In this context it predicts fewer fine particles than the Log-normal distribution and it is generally most accurate for narrow particle size distributions. The interpretation of the cumulative distribution function is that $F(x;k,\lambda )$ is the mass fraction of particles with diameter smaller than $x$ , where $\lambda$ is the mean particle size and $k$ is a measure of the spread of particle sizes.
In describing random point clouds (such as the positions of particles in an ideal gas): the probability to find the nearest-neighbor particle at a distance $x$ from a given particle is given by a Weibull distribution with $k=3$ and $\rho =1/\lambda ^{3}$ equal to the density of the particles.^[15]

Related distributions

The translated Weibull distribution (or 3-parameter Weibull) contains an additional parameter.^[5] It has the probability density function
$f(x;k,\lambda, \theta)={k \over \lambda} \left({x - \theta \over \lambda}\right)^{k-1} e^{-({x-\theta \over \lambda})^k}\,$

for $x \geq \theta$ and $f(x;k,\lambda ,\theta )=0$ for $x<\theta$ , where $k>0$ is the shape parameter, $\lambda >0$ is the scale parameter and $\theta$ is the location parameter of the distribution. When $\theta =0$ , this reduces to the 2-parameter distribution.
The Weibull distribution can be characterized as the distribution of a random variable $W$ such that the random variable
$X = \left(\frac{W}{\lambda}\right)^k$

is the standard exponential distribution with intensity 1.^[5]
This implies that the Weibull distribution can also be characterized in terms of a uniform distribution: if $U$ is uniformly distributed on $(0,1)$ , then the random variable $W = \lambda(-\ln(U))^{1/k}\,$ is Weibull distributed with parameters $k$ and $\lambda$ . (Note that $-\ln(U)$ here is equivalent to $X$ just above.) This leads to an easily implemented numerical scheme for simulating a Weibull distribution.
The Weibull distribution interpolates between the exponential distribution with intensity $1/\lambda$ when $k=1$ and a Rayleigh distribution of mode $\sigma = \lambda/\sqrt{2}$ when $k=2$ .
The Weibull distribution (usually sufficient in reliability engineering) is a special case of the three parameter exponentiated Weibull distribution where the additional exponent equals 1. The exponentiated Weibull distribution accommodates unimodal, bathtub shaped^[16] and monotone failure rates.
The Weibull distribution is a special case of the generalized extreme value distribution. It was in this connection that the distribution was first identified by Maurice Fréchet in 1927.^[17] The closely related Fréchet distribution, named for this work, has the probability density function
$f_{\rm{Frechet}}(x;k,\lambda)=\frac{k}{\lambda} \left(\frac{x}{\lambda}\right)^{-1-k} e^{-(x/\lambda)^{-k}} = -f_{\rm{Weibull}}(x;-k,\lambda).$
The distribution of a random variable that is defined as the minimum of several random variables, each having a different Weibull distribution, is a poly-Weibull distribution.
The Weibull distribution was first applied by Rosin & Rammler (1933) to describe particle size distributions. It is widely used in mineral processing to describe particle size distributions in comminution processes. In this context the cumulative distribution is given by
$f(x;P_{\rm {80}},m)={\begin{cases}1-e^{\ln \left(0.2\right)\left({\frac {x}{P_{\rm {80}}}}\right)^{m}}&x\geq 0,\\0&x<0,\end{cases}}$

where
- $x$ is the particle size
- $P_{\rm{80}}$ is the 80th percentile of the particle size distribution
- $m$ is a parameter describing the spread of the distribution
Because of its availability in spreadsheets, it is also used where the underlying behavior is actually better modeled by an Erlang distribution.^[18]

References

↑ Papoulis, Athanasios Papoulis; Pillai, S. Unnikrishna (2002). Probability, Random Variables, and Stochastic Processes (4th ed.). Boston: McGraw-Hill. ISBN 0-07-366011-6.
↑ "Rayleigh Distribution - MATLAB & Simulink - MathWorks Australia". www.mathworks.com.au.
↑ Jiang, R.; Murthy, D.N.P. (2011). "A study of Weibull shape parameter: Properties and significance". Reliability Engineering & System Safety. 96 (12): 1619–26. doi:10.1016/j.ress.2011.09.003.
↑ Collett, David (2015). Modelling survival data in medical research (3rd ed.). Boca Raton: Chapman and Hall / CRC. ISBN 1439856788.
1 2 3 Johnson, Kotz & Balakrishnan 1994
↑ See (Cheng, Tellambura & Beaulieu 2004) for the case when k is an integer, and (Sagias & Karagiannidis 2005) for the rational case.
↑ Sornette, D. (2004). Critical Phenomena in Natural Science: Chaos, Fractals, Self-organization, and Disorder. .
↑ "1.3.3.30. Weibull Plot". www.itl.nist.gov.
↑ Wayne Nelson (2004) Applied Life Data Analysis. Wiley-Blackwell ISBN 0-471-64462-5
↑ www.statsoft.com. "Survival/Failure Time Analysis". www.statsoft.com.
↑ "Wind Speed Distribution Weibull - REUK.co.uk". www.reuk.co.uk.
↑ "CumFreq, Distribution fitting of probability, free software, cumulative frequency".
↑ Liu, Chao; White, Ryen W.; Dumais, Susan (2010-07-19). "Understanding web browsing behaviors through Weibull analysis of dwell time". ACM: 379–386. doi:10.1145/1835449.1835513. ISBN 9781450301534.
↑ Sharif, M.Nawaz; Islam, M.Nazrul (1980). "The Weibull distribution as a general model for forecasting technological change". Technological Forecasting and Social Change. 18 (3): 247–56. doi:10.1016/0040-1625(80)90026-8.
↑ Chandrashekar, S. (1943). "Stochastic Problems in Physics and Astronomy". Reviews of Modern Physics. 15 (1). p. 86.
↑ "System evolution and reliability of systems". Sysev (Belgium). 2010-01-01.
↑ Montgomery, Douglas. Introduction to statistical quality control. [S.l.]: John Wiley. p. 95. ISBN 9781118146811.
↑ Chatfield, C.; Goodhardt, G.J. (1973). "A Consumer Purchasing Model with Erlang Interpurchase Times". Journal of the American Statistical Association. 68 (344): 828–835. doi:10.1080/01621459.1973.10481432.

Bibliography

Fréchet, Maurice (1927), "Sur la loi de probabilité de l'écart maximum", Annales de la Société Polonaise de Mathematique, Cracovie, 6: 93–116 .
Johnson, Norman L.; Kotz, Samuel; Balakrishnan, N. (1994), 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
Mann, Nancy R.; Schafer, Ray E.; Singpurwalla, Nozer D. (1974), Methods for Statistical Analysis of Reliability and Life Data, Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics (1st ed.), New York: John Wiley & Sons, ISBN 978-0-471-56737-0
Muraleedharan, G.; Rao, A.D.; Kurup, P.G.; Nair, N. Unnikrishnan; Sinha, Mourani (2007), "Modified Weibull Distribution for Maximum and Significant Wave Height Simulation and Prediction", Coastal Engineering, 54 (8): 630–638, doi:10.1016/j.coastaleng.2007.05.001
Rosin, P.; Rammler, E. (1933), "The Laws Governing the Fineness of Powdered Coal", Journal of the Institute of Fuel, 7: 29–36 .
Sagias, N.C.; Karagiannidis, G.K. (2005). "Gaussian Class Multivariate Weibull Distributions: Theory and Applications in Fading Channels". IEEE Transactions on Information Theory. 51 (10): 3608–19. doi:10.1109/TIT.2005.855598. MR 2237527.
Weibull, W. (1951), "A statistical distribution function of wide applicability" (PDF), J. Appl. Mech.-Trans. ASME, 18 (3): 293–297 .
"Weibull Distribution". Engineering statistics handbook. National Institute of Standards and Technology. 2008.
Nelson, Jr, Ralph (2008-02-05). "Dispersing Powders in Liquids, Part 1, Chap 6: Particle Volume Distribution". Retrieved 2008-02-05.

External links

Hazewinkel, Michiel, ed. (2001) [1994], "Weibull distribution", Encyclopedia of Mathematics, Springer Science+Business Media B.V. / Kluwer Academic Publishers, ISBN 978-1-55608-010-4
Mathpages – Weibull analysis
The Weibull Distribution
Reliability Analysis with Weibull
Interactive graphic: Univariate Distribution Relationships
Online Weibull Probability Plotting

This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.

[1] Papoulis, Athanasios Papoulis; Pillai, S. Unnikrishna (2002). Probability, Random Variables, and Stochastic Processes (4th ed.). Boston: McGraw-Hill. ISBN 0-07-366011-6.

[2] "Rayleigh Distribution - MATLAB & Simulink - MathWorks Australia". www.mathworks.com.au.

[3] Jiang, R.; Murthy, D.N.P. (2011). "A study of Weibull shape parameter: Properties and significance". Reliability Engineering & System Safety. 96 (12): 1619–26. doi:10.1016/j.ress.2011.09.003.

[4] Collett, David (2015). Modelling survival data in medical research (3rd ed.). Boca Raton: Chapman and Hall / CRC. ISBN 1439856788.

[JKB-5] 1 2 3 Johnson, Kotz & Balakrishnan 1994

[6] See (Cheng, Tellambura & Beaulieu 2004) for the case when k is an integer, and (Sagias & Karagiannidis 2005) for the rational case.

[7] Sornette, D. (2004). Critical Phenomena in Natural Science: Chaos, Fractals, Self-organization, and Disorder. .

[8] "1.3.3.30. Weibull Plot". www.itl.nist.gov.

[9] Wayne Nelson (2004) Applied Life Data Analysis. Wiley-Blackwell ISBN 0-471-64462-5

[10] www.statsoft.com. "Survival/Failure Time Analysis". www.statsoft.com.

[11] "Wind Speed Distribution Weibull - REUK.co.uk". www.reuk.co.uk.

[12] "CumFreq, Distribution fitting of probability, free software, cumulative frequency".

[13] Liu, Chao; White, Ryen W.; Dumais, Susan (2010-07-19). "Understanding web browsing behaviors through Weibull analysis of dwell time". ACM: 379–386. doi:10.1145/1835449.1835513. ISBN 9781450301534.

[14] Sharif, M.Nawaz; Islam, M.Nazrul (1980). "The Weibull distribution as a general model for forecasting technological change". Technological Forecasting and Social Change. 18 (3): 247–56. doi:10.1016/0040-1625(80)90026-8.

[15] Chandrashekar, S. (1943). "Stochastic Problems in Physics and Astronomy". Reviews of Modern Physics. 15 (1). p. 86.

[16] "System evolution and reliability of systems". Sysev (Belgium). 2010-01-01.

[17] Montgomery, Douglas. Introduction to statistical quality control. [S.l.]: John Wiley. p. 95. ISBN 9781118146811.

[18] Chatfield, C.; Goodhardt, G.J. (1973). "A Consumer Purchasing Model with Erlang Interpurchase Times". Journal of the American Statistical Association. 68 (344): 828–835. doi:10.1080/01621459.1973.10481432.

Probability distributions
List
Discrete univariate with finite support	Benford Bernoulli beta-binomial binomial categorical hypergeometric Poisson binomial Rademacher discrete uniform Zipf Zipf–Mandelbrot
Discrete univariate with infinite support	beta negative binomial Borel Conway–Maxwell–Poisson discrete phase-type Delaporte extended negative binomial Gauss–Kuzmin geometric logarithmic negative binomial parabolic fractal Poisson Skellam Yule–Simon zeta
Continuous univariate supported on a bounded interval	arcsine ARGUS Balding–Nichols Bates beta beta rectangular Irwin–Hall Kumaraswamy logit-normal noncentral beta raised cosine reciprocal triangular U-quadratic uniform Wigner semicircle
Continuous univariate supported on a semi-infinite interval	Benini Benktander 1st kind Benktander 2nd kind beta prime Burr chi-squared chi Dagum Davis exponential-logarithmic Erlang exponential F folded normal Flory–Schulz Fréchet gamma gamma/Gompertz generalized inverse Gaussian Gompertz half-logistic half-normal Hotelling's T-squared hyper-Erlang hyperexponential hypoexponential inverse chi-squared scaled inverse chi-squared inverse Gaussian inverse gamma Kolmogorov Lévy log-Cauchy log-Laplace log-logistic log-normal Lomax matrix-exponential Maxwell–Boltzmann Maxwell–Jüttner Mittag-Leffler Nakagami noncentral chi-squared Pareto phase-type poly-Weibull Rayleigh relativistic Breit–Wigner Rice shifted Gompertz truncated normal type-2 Gumbel Weibull Discrete Weibull Wilks's lambda
Continuous univariate supported on the whole real line	Cauchy exponential power Fisher's z Gaussian q generalized normal generalized hyperbolic geometric stable Gumbel Holtsmark hyperbolic secant Johnson's S_U Landau Laplace asymmetric Laplace logistic noncentral t normal (Gaussian) normal-inverse Gaussian skew normal slash stable Student's t type-1 Gumbel Tracy–Widom variance-gamma Voigt
Continuous univariate with support whose type varies	generalized extreme value generalized Pareto Marchenko–Pastur q-exponential q-Gaussian q-Weibull shifted log-logistic Tukey lambda
Mixed continuous-discrete univariate	rectified Gaussian
Multivariate (joint)	Discrete Ewens multinomial Dirichlet-multinomial negative multinomial Continuous Dirichlet generalized Dirichlet multivariate Laplace multivariate normal multivariate stable multivariate t normal-inverse-gamma normal-gamma Matrix-valued inverse matrix gamma inverse-Wishart matrix normal matrix t matrix gamma normal-inverse-Wishart normal-Wishart Wishart
Directional	Univariate (circular) directional Circular uniform univariate von Mises wrapped normal wrapped Cauchy wrapped exponential wrapped asymmetric Laplace wrapped Lévy Bivariate (spherical) Kent Bivariate (toroidal) bivariate von Mises Multivariate von Mises–Fisher Bingham
Degenerate and singular	Degenerate Dirac delta function Singular Cantor
Families	Circular compound Poisson elliptical exponential natural exponential location–scale maximum entropy mixture Pearson Tweedie wrapped

Weibull (2-parameter)
Probability density function
Cumulative distribution function
Parameters	$\lambda\in (0, +\infty)\,$ scale $k\in (0, +\infty)\,$ shape
Support	$x \in [0, +\infty)\,$
PDF	$f(x)={\begin{cases}{\frac {k}{\lambda }}\left({\frac {x}{\lambda }}\right)^{k-1}e^{-(x/\lambda )^{k}}&x\geq 0\\0&x<0\end{cases}}$
CDF	$\begin{cases}1- e^{-(x/\lambda)^k} & x\geq0\\ 0 & x<0\end{cases}$
Mean	$\lambda \, \Gamma(1+1/k)\,$
Median	$\lambda (\ln 2)^{1/k}\,$
Mode	${\begin{cases}\lambda \left({\frac {k-1}{k}}\right)^{1/k}\,&k>1\\0&k\leq 1\end{cases}}$
Variance	$\lambda^2\left[\Gamma\left(1+\frac{2}{k}\right) - \left(\Gamma\left(1+\frac{1}{k}\right)\right)^2\right]\,$
Skewness	$\frac{\Gamma(1+3/k)\lambda^3-3\mu\sigma^2-\mu^3}{\sigma^3}$
Ex. kurtosis	(see text)
Entropy	$\gamma(1-1/k)+\ln(\lambda/k)+1 \,$
MGF	$\sum_{n=0}^\infty \frac{t^n\lambda^n}{n!}\Gamma(1+n/k), \ k\geq1$
CF	$\sum_{n=0}^\infty \frac{(it)^n\lambda^n}{n!}\Gamma(1+n/k)$