Difference between revisions of "Weibull distribution"

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= Library =
 
= Library =
  
Distributions
+
Distribution
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 +
= Example =
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:Weibull(10,4) → [[Image:Weibull_graph.jpg]]
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= Parameter Estimation =
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Suppose you have sampled historic data in ''Data'', indexed by ''I'', and you want to find the parameters for the best-fit Weibull distribution.  The parameters can be estimated using a linear regression as follows:
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[[Index..Do|Index]] bm := ['b','m'];
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[[Var..Do|Var]] Fx :=  ([[Rank]](Data,I) - 0.5) / [[Size]](I);
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[[Var..Do|Var]] Z := [[Ln]](-[[Ln]](1-Fx));
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[[Var..Do|Var]] fit := [[Regression]](Z,[[Array]](bm,[1,[[Ln]](x)]),I,bm);
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[[Var..Do|Var]] shape := fit[bm='m'];
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[[Var..Do|Var]] b := fit[bm='b'];
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[[Var..Do|Var]] scale := [[Exp]](-b/shape);
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[shape,scale]
  
 
= See Also =
 
= See Also =
  
 
* [[Gamma]], [[Normal]]
 
* [[Gamma]], [[Normal]]

Revision as of 21:14, 3 March 2009


Weibull( shape, scale )

The Weibull distribution is often used to represent failure time in reliability models. It is similar in shape to the gamma distribution, but tends to be less skewed and tail-heavy. It is a continuous distribution over the positive real numbers.

The Weibull distribution has a cumulative density given by

Weibull cdf eq.jpg

Library

Distribution

Example

Weibull(10,4) → Weibull graph.jpg

Parameter Estimation

Suppose you have sampled historic data in Data, indexed by I, and you want to find the parameters for the best-fit Weibull distribution. The parameters can be estimated using a linear regression as follows:

Index bm := ['b','m'];
Var Fx :=  (Rank(Data,I) - 0.5) / Size(I);
Var Z := Ln(-Ln(1-Fx));
Var fit := Regression(Z,Array(bm,[1,Ln(x)]),I,bm);
Var shape := fit[bm='m'];
Var b := fit[bm='b'];
Var scale := Exp(-b/shape);
[shape,scale]

See Also

Comments


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