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==The Gumbel/SEV Distribution==
#REDIRECT [[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  <math>pdf</math>  is skewed to the left, unlike the Weibull distribution's  <math>pdf</math> , 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). [27]
{{gumbel probability density function}}
 
{{gumbel mean median and mode}}
 
===The Gumbel Standard Deviation===
The standard deviation for the Gumbel distribution is given by:
 
::<math>{{\sigma }_{T}}=\sigma \pi \frac{\sqrt{6}}{6}</math>
 
 
===The Gumbel Reliability Function===
The reliability for a mission of time  <math>T</math>  for the Gumbel distribution is given by:
 
::<math>R(T)={{e}^{-{{e}^{z}}}}</math>
 
The unreliability function is given by:
 
::<math>F(T)=1-{{e}^{-{{e}^{z}}}}</math>
 
===The Gumbel Reliable Life===
The Gumbel reliable life is given by:
 
 
::<math>{{T}_{R}}=\mu +\sigma [\ln (-\ln (R))]</math>
 
 
===The Gumbel Failure Rate Function===
The instantaneous Gumbel failure rate is given by:
 
::<math>\lambda =\frac{{{e}^{z}}}{\sigma }</math>
 
 
===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  <math>pdf</math>  has no shape parameter. This means that the Gumbel  <math>pdf</math>  has only one shape, which does not change.
:• The Gumbel  <math>pdf</math>  has location parameter  <math>\mu ,</math>  which is equal to the mode  <math>\tilde{T},</math>  but it differs from median and mean. This is because the Gumbel distribution is not symmetrical about its  <math>\mu </math> .
:• As  <math>\mu </math>  decreases, the  <math>pdf</math>  is shifted to the left.
:• As  <math>\mu </math>  increases, the  <math>pdf</math>  is shifted to the right.
 
[[Image:effectofmuongumbel.gif|thumb|center|400px| ]]  
 
:• As  <math>\sigma </math>  increases, the  <math>pdf</math>  spreads out and becomes shallower.
:• As  <math>\sigma </math>  decreases, the  <math>pdf</math>  becomes taller and narrower.
:• For  <math>T=\pm \infty ,</math>  <math>pdf=0.</math>  For  <math>T=\mu </math> , the  <math>pdf</math>  reaches its maximum point <math>\frac{1}{\sigma e}</math>
 
[[Image:effectofsigmaongumbel.gif|thumb|center|400px| ]]
 
:• The points of inflection of the  <math>pdf</math>  graph are  <math>T=\mu \pm \sigma \ln (\tfrac{3\pm \sqrt{5}}{2})</math>  or  <math>T\approx \mu \pm \sigma 0.96242</math> .
:• If times follow the Weibull distribution, then the logarithm of times follow a Gumbel distribution. If  <math>{{t}_{i}}</math>  follows a Weibull distribution with  <math>\beta </math>  and  <math>\eta </math>  , then the  <math>Ln({{t}_{i}})</math>  follows a Gumbel distribution with  <math>\mu =\ln (\eta )</math>  and  <math>\sigma =\tfrac{1}{\beta }</math>  [32] <math>.</math>
 
===Probability Paper===
The form of the Gumbel probability paper is based on a linearization of the  <math>cdf</math> . From Eqn. (UnrGumbel):
 
::<math>z=\ln (-\ln (1-F))</math>
 
 
:using Eqns. (z3):
 
::<math>\frac{T-\mu }{\sigma }=\ln (-\ln (1-F))</math>
 
 
:Then:
 
::<math>\ln (-\ln (1-F))=-\frac{\mu }{\sigma }+\frac{1}{\sigma }T</math>
 
 
:Now let:
 
::<math>y=\ln (-\ln (1-F))</math>
 
 
::<math>x=T</math>
 
 
:and:
 
::<math>\begin{align}
  & a= & -\frac{\mu }{\sigma } \\
& b= & \frac{1}{\sigma } 
\end{align}</math>
 
 
which results in the linear equation of:
 
::<math>y=a+bx</math>
 
 
The Gumbel probability paper resulting from this linearized  <math>cdf</math>  function is shown next.
 
[[Image:probabilitygumbel.gif|thumb|center|400px| ]]
 
For  <math>z=0</math> ,  <math>T=\mu </math>  and  <math>R(t)={{e}^{-{{e}^{0}}}}\approx 0.3678</math>  (63.21% unreliability). For  <math>z=1</math> ,  <math>\sigma =T-\mu </math>  and  <math>R(t)={{e}^{-{{e}^{1}}}}\approx 0.0659.</math>  To read  <math>\mu </math>  from the plot, find the time value that corresponds to the intersection of the probability plot with the 63.21% unreliability line. To read  <math>\sigma </math>  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  <math>\mu </math>  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 Chapter 5. Only Fisher Matrix confidence bounds are available for the Gumbel distribution.
 
====Bounds on the Parameters====
The lower and upper bounds on the mean,  <math>\widehat{\mu }</math> , are estimated from:
 
::<math>\begin{align}
  & {{\mu }_{U}}= & \widehat{\mu }+{{K}_{\alpha }}\sqrt{Var(\widehat{\mu })}\text{ (upper bound)} \\
& {{\mu }_{L}}= & \widehat{\mu }-{{K}_{\alpha }}\sqrt{Var(\widehat{\mu })}\text{ (lower bound)} 
\end{align}</math>
 
 
Since the standard deviation,  <math>\widehat{\sigma }</math> , must be positive, then  <math>\ln (\widehat{\sigma })</math>  is treated as normally distributed, and the bounds are estimated from:
 
::<math>\begin{align}
  & {{\sigma }_{U}}= & \widehat{\sigma }\cdot {{e}^{\tfrac{{{K}_{\alpha }}\sqrt{Var(\widehat{\sigma })}}{{{\widehat{\sigma }}_{T}}}}}\text{ (upper bound)} \\
& {{\sigma }_{L}}= & \frac{\widehat{\sigma }}{{{e}^{\tfrac{{{K}_{\alpha }}\sqrt{Var(\widehat{\sigma })}}{\widehat{\sigma }}}}}\text{ (lower bound)} 
\end{align}</math>
 
where  <math>{{K}_{\alpha }}</math>  is defined by:
 
::<math>\alpha =\frac{1}{\sqrt{2\pi }}\int_{{{K}_{\alpha }}}^{\infty }{{e}^{-\tfrac{{{t}^{2}}}{2}}}dt=1-\Phi ({{K}_{\alpha }})</math>
 
 
If  <math>\delta </math>  is the confidence level, then  <math>\alpha =\tfrac{1-\delta }{2}</math>  for the two-sided bounds, and  <math>\alpha =1-\delta </math>  for the one-sided bounds.
 
The variances and covariances of  <math>\widehat{\mu }</math>  and  <math>\widehat{\sigma }</math>  are estimated from the Fisher matrix as follows:
 
::<math>\left( \begin{matrix}
  \widehat{Var}\left( \widehat{\mu } \right) & \widehat{Cov}\left( \widehat{\mu },\widehat{\sigma } \right)  \\
  \widehat{Cov}\left( \widehat{\mu },\widehat{\sigma } \right) & \widehat{Var}\left( \widehat{\sigma } \right)  \\
\end{matrix} \right)=\left( \begin{matrix}
  -\tfrac{{{\partial }^{2}}\Lambda }{\partial {{\mu }^{2}}} & -\tfrac{{{\partial }^{2}}\Lambda }{\partial \mu \partial \sigma }  \\
  {} & {}  \\
  -\tfrac{{{\partial }^{2}}\Lambda }{\partial \mu \partial \sigma } & -\tfrac{{{\partial }^{2}}\Lambda }{\partial {{\sigma }^{2}}}  \\
\end{matrix} \right)_{\mu =\widehat{\mu },\sigma =\widehat{\sigma }}^{-1}</math>
 
 
<math>\Lambda </math>  is the log-likelihood function of the Gumbel distribution, described in Chapter 3 and Appendix C.
 
====Bounds on Reliability====
The reliability of the Gumbel distribution is given by: 
 
::<math>\widehat{R}(T;\hat{\mu },\hat{\sigma })={{e}^{-{{e}^{{\hat{z}}}}}}</math>
 
:where:
 
::<math>\widehat{z}=\frac{t-\widehat{\mu }}{\widehat{\sigma }}</math>
 
The bounds on  <math>z</math>  are estimated from:
 
::<math>\begin{align}
  & {{z}_{U}}= & \widehat{z}+{{K}_{\alpha }}\sqrt{Var(\widehat{z})} \\
& {{z}_{L}}= & \widehat{z}-{{K}_{\alpha }}\sqrt{Var(\widehat{z})} 
\end{align}</math>
 
:where:
 
::<math>Var(\widehat{z})={{\left( \frac{\partial z}{\partial \mu } \right)}^{2}}Var(\widehat{\mu })+{{\left( \frac{\partial z}{\partial \sigma } \right)}^{2}}Var(\widehat{\sigma })+2\left( \frac{\partial z}{\partial \mu } \right)\left( \frac{\partial z}{\partial \sigma } \right)Cov\left( \widehat{\mu },\widehat{\sigma } \right)</math>
 
:or:
 
::<math>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]</math>
 
 
The upper and lower bounds on reliability are:
 
::<math>\begin{align}
  & {{R}_{U}}= & {{e}^{-{{e}^{{{z}_{L}}}}}}\text{ (upper bound)} \\
& {{R}_{L}}= & {{e}^{-{{e}^{{{z}_{U}}}}}}\text{ (lower bound)} 
\end{align}</math>
 
====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:
 
::<math>\widehat{T}(\widehat{\mu },\widehat{\sigma })=\widehat{\mu }+\widehat{\sigma }z</math>
 
:where:
 
::<math>z=\ln (-\ln (R))</math>
 
::<math>Var(\widehat{T})={{(\frac{\partial T}{\partial \mu })}^{2}}Var(\widehat{\mu })+2(\frac{\partial T}{\partial \mu })(\frac{\partial T}{\partial \sigma })Cov(\widehat{\mu },\widehat{\sigma })+{{(\frac{\partial T}{\partial \sigma })}^{2}}Var(\widehat{\sigma })</math>
 
:or:
 
::<math>Var(\widehat{T})=Var(\widehat{\mu })+2\widehat{z}Cov(\widehat{\mu },\widehat{\sigma })+{{\widehat{z}}^{2}}Var(\widehat{\sigma })</math>
 
 
The upper and lower bounds are then found by:
 
::<math>\begin{align}
  & {{T}_{U}}= & \hat{T}+{{K}_{\alpha }}\sqrt{Var(\hat{T})}\text{ (Upper bound)} \\
& {{T}_{L}}= & \hat{T}-{{K}_{\alpha }}\sqrt{Var(\hat{T})}\text{ (Lower bound)} 
\end{align}</math>
 
====A Gumbel Distribution Example====
Verify using Monte Carlo simulation that if  <math>{{t}_{i}}</math>  follows a Weibull distribution with  <math>\beta </math>  and  <math>\eta </math> , then the  <math>Ln({{t}_{i}})</math>  follows a Gumbel distribution with  <math>\mu =\ln (\eta )</math>  and  <math>\sigma =1/\beta ).</math>
 
Let us assume that  <math>{{t}_{i}}</math>  follows a Weibull distribution with  <math>\beta =0.5</math>  and  <math>\eta =10000.</math>  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.
 
[[Image:montecarlo4eva.png|thumb|center|400px| ]]
 
After obtaining the random time values  <math>{{t}_{i}}</math> , insert a new Data Sheet using the Insert Data Sheet option under the Folio menu. In this sheet enter the  <math>Ln({{t}_{i}})</math>  values using the LN function and referring to the cells in the sheet that contains the  <math>{{t}_{i}}</math>  values. Delete any negative values, if there are any, since Weibull++ expects time values to be positive. Calculate the parameters of the Gumbel distribution that fits the  <math>Ln({{t}_{i}})</math>  values.
 
Using maximum likelihood as the analysis method, the estimated parameters are:
 
::<math>\begin{align}
  & \hat{\mu }= & 9.3816 \\
& \hat{\sigma }= & 1.9717 
\end{align}</math>
 
 
Since  <math>\ln (\eta )=</math>  9.2103 ( <math>\simeq 9.3816</math> ) and  <math>1/\beta =2</math>  <math>(\simeq 1.9717),</math>  then this simulation verifies that  <math>Ln({{t}_{i}})</math>  follows a Gumbel distribution with  <math>\mu =\ln (\eta )</math>  and  <math>\delta =1/\beta .</math>
 
Note: This example illustrates a property of the Gumbel distribution; it is not meant to be a formal proof.

Latest revision as of 08:56, 3 August 2012

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