The Gumbel/SEV Distribution
The Gumbel distribution is also referred to as the Smallest Extreme Value (SEV) distribution or the Smallest Extreme Value (Type I) distribution. The Gumbel distribution's pdf is skewed to the left, unlike the Weibull distribution's pdf, which is skewed to the right. The Gumbel distribution is appropriate for modeling strength, which is sometimes skewed to the left (few weak units in the lower tail, most units in the upper tail of the strength population). The Gumbel distribution could also be appropriate for modeling the life of products that experience very quick wear-out after reaching a certain age. The distribution of logarithms of times can often be modeled with the Gumbel distribution (in addition to the more common lognormal distribution), as discussed in Meeker and Escobar [27].
Gumbel Probability Density Function
The pdf of the Gumbel distribution is given by:
where:
and:
Gumbel Distribution Functions
Mean, Median and Mode
The Gumbel mean or MTTF is:
where 
(Euler's constant).
The mode of the Gumbel distribution is:
The median of the Gumbel distribution is:
Standard Deviation
The standard deviation for the Gumbel distribution is given by:
The Gumbel Reliability Function
The reliability for a mission of time
for the Gumbel distribution is given by:
The unreliability function is given by:
The Gumbel Reliable Life
The Gumbel reliable life is given by:
![{\displaystyle {\begin{aligned}{{t}_{R}}=\mu +\sigma [\ln(-\ln(R))]\end{aligned}}\,\!}](https://en.wikipedia.org/api/rest_v1/media/math/render/svg/190a3e22976cc29205da9f1e2ddc06be23e93990)
The Gumbel Failure Rate Function
The instantaneous Gumbel failure rate is given by:
Characteristics of the Gumbel Distribution
Some of the specific characteristics of the Gumbel distribution are the following:
-
- The shape of the Gumbel distribution is skewed to the left. The Gumbel pdf has no shape parameter. This means that the Gumbel pdf has only one shape, which does not change.
- The Gumbel pdf has location parameter
which is equal to the mode
but it differs from median and mean. This is because the Gumbel distribution is not symmetrical about its
. - As
decreases, the
pdf is shifted to the left. - As
increases, the
pdf is shifted to the right.
-
- As
increases, the
pdf spreads out and becomes shallower. - As
decreases, the
pdf becomes taller and narrower. - For
pdf=0. For 
, the
pdf reaches its maximum point
- As
-
- The points of inflection of the pdf graph are
or
. - If times follow the Weibull distribution, then the logarithm of times follow a Gumbel distribution. If
follows a Weibull distribution with
and
, then the
follows a Gumbel distribution with
and
, as discussed in
[32].
- The points of inflection of the pdf graph are
Probability Paper
The form of the Gumbel probability paper is based on a linearization of the cdf. From the unreliabililty equation, we know:
using the equation for z, we get:
Then:
Now let:
and:
which results in the linear equation of:
The Gumbel probability paper resulting from this linearized cdf function is shown next.
For 
,
and
(63.21% unreliability). For
,
and
To read
from the plot, find the time value that corresponds to the intersection of the probability plot with the 63.21% unreliability line. To read
from the plot, find the time value that corresponds to the intersection of the probability plot with the 93.40% unreliability line, then take the difference between this time value and the
value.
Confidence Bounds
This section presents the method used by the application to estimate the different types of confidence bounds for data that follow the Gumbel distribution. The complete derivations were presented in detail (for a general function) in Confidence Bounds. Only Fisher Matrix confidence bounds are available for the Gumbel distribution.
Bounds on the Parameters
The lower and upper bounds on the mean,
, are estimated from:
Since the standard deviation, 
, must be positive, then
is treated as normally distributed, and the bounds are estimated from:
where 
is defined by:
If 
is the confidence level, then
for the two-sided bounds, and
for the one-sided bounds.
The variances and covariances of 
and
are estimated from the Fisher matrix as follows:
is the log-likelihood function of the Gumbel distribution, described in
Parameter Estimation and
Appendix D.
Bounds on Reliability
The reliability of the Gumbel distribution is given by:
where:
The bounds on 
are estimated from:
where:
or:
![{\displaystyle Var({\widehat {z}})={\frac {1}{{\widehat {\sigma }}^{2}}}\left[Var({\widehat {\mu }})+{{\widehat {z}}^{2}}Var({\widehat {\sigma }})+2\cdot {\widehat {z}}\cdot Cov\left({\widehat {\mu }},{\widehat {\sigma }}\right)\right]\,\!}](https://en.wikipedia.org/api/rest_v1/media/math/render/svg/ae8213c72a64d91e1d4a46e011e0aac9544c678c)
The upper and lower bounds on reliability are:
Bounds on Time
The bounds around time for a given Gumbel percentile (unreliability) are estimated by first solving the reliability equation with respect to time, as follows:
where:
or:
The upper and lower bounds are then found by:
Example
Verify using Monte Carlo simulation that if
follows a Weibull distribution with
and
, then the
follows a Gumbel distribution with
and
.
Let us assume that 
follows a Weibull distribution with
and
. The Monte Carlo simulation tool in Weibull++ can be used to generate a set of random numbers that follow a Weibull distribution with the specified parameters. The following picture shows the Main tab of the Monte Carlo Data Generation utility.
On the Settings tab, set the number of points to 100 and click
Generate. This creates a new data sheet in the folio that contains random time values
.
Insert a new data sheet in the folio and enter the corresponding
values of the time values generated by the Monte Carlo simulation. Delete any negative values, if there are any, because Weibull++ expects the time values to be positive. After obtaining the
values, analyze the data sheet using the Gumbel distribution and the MLE parameter estimation method. The estimated parameters are (your results may vary due to the random numbers generated by simulation):
Because 
(
) and
, then this simulation verifies that
follows a Gumbel distribution with
and
.
Note: This example illustrates a property of the Gumbel distribution; it is not meant to be a formal proof.