Template:Bounds on the Parameters FMB ED: Difference between revisions

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where <math>\Lambda </math> is the log-likelihood function of the exponential distribution, described in [[Appendix: Distribution Log-Likelihood Equations|Appendix for log-likelihood function]].
where <math>\Lambda </math> is the log-likelihood function of the exponential distribution, described in [[Appendix:_Log-Likelihood_Equations|an appendix]].


Note that no true MLE solution exists for the case of the two-parameter exponential distribution. The mathematics simply break down while trying to simultaneously solve the partial derivative equations for both the <math>\gamma </math> and <math>\lambda </math> parameters, resulting in unrealistic conditions. The way around this conundrum involves setting <math>\gamma ={{t}_{1}},</math> or the first time-to-failure, and calculating <math>\lambda </math> in the regular fashion for this methodology. Weibull++ treats <math>\gamma </math> as a constant when computing bounds, i.e. <math>Var(\hat{\gamma })=0.</math> (See the discussion in [[Appendix: Distribution Log-Likelihood Equations|Appendix for log-likelihood function]] for more information.)
Note that no true MLE solution exists for the case of the two-parameter exponential distribution. The mathematics simply break down while trying to simultaneously solve the partial derivative equations for both the <math>\gamma </math> and <math>\lambda </math> parameters, resulting in unrealistic conditions. The way around this conundrum involves setting <math>\gamma ={{t}_{1}},</math> or the first time-to-failure, and calculating <math>\lambda </math> in the regular fashion for this methodology. Weibull++ treats <math>\gamma </math> as a constant when computing bounds, i.e. <math>Var(\hat{\gamma })=0.</math> (See the discussion in [[Appendix:_Log-Likelihood_Equations|the appendix]] for more information.)

Revision as of 08:52, 3 August 2012

Bounds on the Parameters

For the failure rate [math]\displaystyle{ \hat{\lambda } }[/math] the upper ([math]\displaystyle{ {{\lambda }_{U}} }[/math]) and lower ([math]\displaystyle{ {{\lambda }_{L}} }[/math]) bounds are estimated by [30]:


[math]\displaystyle{ \begin{align} & {{\lambda }_{U}}= & \hat{\lambda }\cdot {{e}^{\left[ \tfrac{{{K}_{\alpha }}\sqrt{Var(\hat{\lambda })}}{\hat{\lambda }} \right]}} \\ & & \\ & {{\lambda }_{L}}= & \frac{\hat{\lambda }}{{{e}^{\left[ \tfrac{{{K}_{\alpha }}\sqrt{Var(\hat{\lambda })}}{\hat{\lambda }} \right]}}} \end{align} }[/math]

where [math]\displaystyle{ {{K}_{\alpha }} }[/math] is defined by:


[math]\displaystyle{ \alpha =\frac{1}{\sqrt{2\pi }}\int_{{{K}_{\alpha }}}^{\infty }{{e}^{-\tfrac{{{t}^{2}}}{2}}}dt=1-\Phi ({{K}_{\alpha }}) }[/math]


If [math]\displaystyle{ \delta }[/math] is the confidence level, then [math]\displaystyle{ \alpha =\tfrac{1-\delta }{2} }[/math] for the two-sided bounds, and [math]\displaystyle{ \alpha =1-\delta }[/math] for the one-sided bounds. The variance of [math]\displaystyle{ \hat{\lambda }, }[/math] [math]\displaystyle{ Var(\hat{\lambda }), }[/math] is estimated from the Fisher matrix, as follows:


[math]\displaystyle{ Var(\hat{\lambda })={{\left( -\frac{{{\partial }^{2}}\Lambda }{\partial {{\lambda }^{2}}} \right)}^{-1}} }[/math]


where [math]\displaystyle{ \Lambda }[/math] is the log-likelihood function of the exponential distribution, described in an appendix.

Note that no true MLE solution exists for the case of the two-parameter exponential distribution. The mathematics simply break down while trying to simultaneously solve the partial derivative equations for both the [math]\displaystyle{ \gamma }[/math] and [math]\displaystyle{ \lambda }[/math] parameters, resulting in unrealistic conditions. The way around this conundrum involves setting [math]\displaystyle{ \gamma ={{t}_{1}}, }[/math] or the first time-to-failure, and calculating [math]\displaystyle{ \lambda }[/math] in the regular fashion for this methodology. Weibull++ treats [math]\displaystyle{ \gamma }[/math] as a constant when computing bounds, i.e. [math]\displaystyle{ Var(\hat{\gamma })=0. }[/math] (See the discussion in the appendix for more information.)