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(Created page with '===The Gumbel Distribution=== The Gumbel distribution is also referred to as the Smallest Extreme Value (SEV) distribution or the Smallest Extreme Value (Type 1) distribution. Th…') |
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f(t)= & \frac{1}{\sigma }{{e}^{z-{e^z}}} \\ | f(t)= & \frac{1}{\sigma }{{e}^{z-{e^z}}} \\ | ||
z= &\frac{t-\mu }{\sigma } \\ | z= &\frac{t-\mu }{\sigma } \\ | ||
f( | f(t)\ge & 0,\sigma >0 | ||
\end{align}</math> | \end{align}</math> | ||
<br> | <br> | ||
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\end{align}</math> | \end{align}</math> | ||
The Gumbel distribution and its characteristics are presented in more detail in Chapter | The Gumbel distribution and its characteristics are presented in more detail in [[The Gumbel/SEV Distribution | Chapter 16]]. | ||
<br> | <br> | ||
Revision as of 21:27, 3 February 2012
The Gumbel Distribution
The Gumbel distribution is also referred to as the Smallest Extreme Value (SEV) distribution or the Smallest Extreme Value (Type 1) distribution. The Gumbel distribution is appropriate for modeling strength, which is sometimes skewed to the left (few weak units fail under low stress, while the rest fail at higher stresses). 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 [math]\displaystyle{ pdf }[/math] of the Gumbel distribution is given by:
- [math]\displaystyle{ \begin{align} f(t)= & \frac{1}{\sigma }{{e}^{z-{e^z}}} \\ z= &\frac{t-\mu }{\sigma } \\ f(t)\ge & 0,\sigma \gt 0 \end{align} }[/math]
- where,
- [math]\displaystyle{ \begin{align} \mu = & \text{location parameter} \\ \sigma = & \text{scale parameter} \end{align} }[/math]
The Gumbel distribution and its characteristics are presented in more detail in Chapter 16.