# Weibull distribution

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In probability theory and statistics, the **Weibull distribution** Template:IPAc-en is a continuous probability distribution. It is named after Waloddi Weibull, who described it in detail in 1951, although it was first identified by Template:Harvtxt and first applied by Template:Harvtxt to describe a particle size distribution.

## Contents

## Definition

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

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 ^{[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:

- A value of
*k*< 1 indicates that the failure rate decreases over time. 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. - 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. - 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 field of materials science, the shape parameter *k* of a distribution of strengths is known as the Weibull modulus.

## 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. It is interesting to note that 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.

### Distribution function

The cumulative distribution function for the Weibull distribution is

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

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

for 0 ≤ p < 1.

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

### Moments

The moment generating function of the logarithm of a Weibull distributed random variable is given by^{[3]}

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

In particular, the *n*th raw moment of *X* is given by

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

and

The skewness is given by

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

The excess kurtosis is given by

where . The kurtosis excess may also be written as:

### 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

Alternatively, one can attempt to deal directly with the integral

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.^{[4]} With *t* replaced by −*t*, one finds

where *G* is the Meijer G-function.

The characteristic function has also been obtained by Template:Harvtxt.

### Information entropy

The information entropy is given by

where is the Euler–Mascheroni constant.

### Parameter estimation

#### Maximum likelihood

The maximum likelihood estimator for the parameter given is,^{[5]}

Where are the largest observed samples.

The maximum likelihood estimator for is,

This being an implicit function, one must generally solve for by numerical means.

## Weibull plot

The fit of data to a Weibull distribution can be visually assessed using a Weibull Plot.^{[6]} The Weibull Plot is a plot of the empirical cumulative distribution function of data on special axes in a type of Q-Q plot. The axes are versus . The reason for this change of variables is the cumulative distribution function can be linearized:

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 where is the rank of the data point and is the number of data points.^{[7]}

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 and the scale parameter can also be inferred.

The Weibull distribution is used{{ safesubst:#invoke:Unsubst||date=__DATE__ |$B=
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- In survival analysis
^{[8]} - In reliability engineering and failure analysis
- In industrial engineering to represent manufacturing and delivery times
- In extreme value theory
- In weather forecasting
- To describe wind speed distributions, as the natural distribution often matches the Weibull shape
^{[9]}

- To describe wind speed distributions, as the natural distribution often matches the Weibull shape
- 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

- 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)
^{[10]}

- 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.{{ safesubst:#invoke:Unsubst||date=__DATE__ |$B=

{{#invoke:Category handler|main}}{{#invoke:Category handler|main}}^{[citation needed]}
}} In this context it predicts fewer fine particles than the Log-normal distribution and it is generally most accurate for narrow particle size distributions.{{ safesubst:#invoke:Unsubst||date=__DATE__ |$B=
{{#invoke:Category handler|main}}{{#invoke:Category handler|main}}^{[citation needed]}
}} The interpretation of the cumulative distribution function is that *F*(*x; k; λ*) is the mass fraction of particles with diameter smaller than *x*, where *λ* is the mean particle size and *k* is a measure of the spread of particle sizes.

## Related distributions

- The translated Weibull distribution contains an additional parameter.
^{[3]}It has the probability density function

for and *f*(*x*; *k*, λ, θ) = 0 for *x* < θ, where is the shape parameter, is the scale parameter and is the location parameter of the distribution. When θ=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

is the standard exponential distribution with intensity 1.^{[3]}

- 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 is Weibull distributed with parameters*k*and λ. (Note that here is equivalent to 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/λ when
*k*= 1 and a Rayleigh distribution of mode 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*
^{[11]}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.
^{[12]}The closely related Fréchet distribution, named for this work, has the probability density function

- 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 Template:Harvtxt 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

where

- : Particle size
- : 80th percentile of the particle size distribution
- : 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.
^{[13]}

## See also

- Fisher–Tippett–Gnedenko theorem
- Logistic distribution
- Rosin-Rammler distribution for particle size analysis

## References

- ↑ Papoulis, Pillai, "Probability, Random Variables, and Stochastic Processes, 4th Edition
- ↑ http://www.mathworks.com.au/help/stats/rayleigh-distribution.html
- ↑
^{3.0}^{3.1}^{3.2}Template:Harvnb - ↑ See Template:Harv for the case when
*k*is an integer, and Template:Harv for the rational case. - ↑ {{#invoke:citation/CS1|citation |CitationClass=book }}.
- ↑ The Weibull plot
- ↑ Wayne Nelson (2004)
*Applied Life Data Analysis*. Wiley-Blackwell ISBN 0-471-64462-5 - ↑ Survival/Failure Time Analysis
- ↑ Wind Speed Distribution Weibull
- ↑ Template:Cite web
- ↑ Template:Cite web
- ↑ {{#invoke:citation/CS1|citation |CitationClass=book }}
- ↑ C. Chatfield and G.J. Goodhardt: “A Consumer Purchasing Model with Erlang Interpurchase Times”;
*Journal of the American Statistical Association*, Dec. 1973, Vol.68, pp.828-835

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## External links

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- Mathpages - Weibull Analysis
- The Weibull Distribution
- Reliability Analysis with Weibull
- Interactive graphic: Univariate Distribution Relationships

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