Template:Eyring-ex cb on mean life: Difference between revisions

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(Created page with '===Confidence Bounds on Mean Life=== <br> The mean life for the Eyring relationship l is given by Eqn. (eyring) by setting <math>m=L(V)</math> . The upper <math>({{m}_{U}})</m…')
 
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The mean life for the Eyring relationship l is given by Eqn. (eyring) by setting <math>m=L(V)</math> . The upper  <math>({{m}_{U}})</math>  and lower  <math>({{m}_{L}})</math>  bounds on the mean life (ML estimate of the mean life) are estimated by:
The mean life for the Eyring relationship is given by setting <math>m=L(V)</math> . The upper  <math>({{m}_{U}})</math>  and lower  <math>({{m}_{L}})</math>  bounds on the mean life (ML estimate of the mean life) are estimated by:


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Revision as of 00:24, 15 February 2012

Confidence Bounds on Mean Life


The mean life for the Eyring relationship is given by setting [math]\displaystyle{ m=L(V) }[/math] . The upper [math]\displaystyle{ ({{m}_{U}}) }[/math] and lower [math]\displaystyle{ ({{m}_{L}}) }[/math] bounds on the mean life (ML estimate of the mean life) are estimated by:


[math]\displaystyle{ {{m}_{U}}=\widehat{m}\cdot {{e}^{\tfrac{{{K}_{\alpha }}\sqrt{Var(\widehat{m})}}{\widehat{m}}}} }[/math]


[math]\displaystyle{ {{m}_{L}}=\widehat{m}\cdot {{e}^{-\tfrac{{{K}_{\alpha }}\sqrt{Var(\widehat{m})}}{\widehat{m}}}} }[/math]


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


[math]\displaystyle{ \alpha =\frac{1}{\sqrt{2\pi }}\mathop{}_{{{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{ \widehat{m} }[/math] is given by:


[math]\displaystyle{ \begin{align} & Var(\widehat{m})= & {{\left( \frac{\partial m}{\partial A} \right)}^{2}}Var(\widehat{A})+{{\left( \frac{\partial m}{\partial B} \right)}^{2}}Var(\widehat{B}) \\ & & +2\left( \frac{\partial m}{\partial A} \right)\left( \frac{\partial m}{\partial B} \right)Cov(\widehat{A},\widehat{B}) \end{align} }[/math]


or:


[math]\displaystyle{ Var(\widehat{m})=\frac{1}{{{V}^{2}}}{{e}^{-2\left( \widehat{A}-\tfrac{\widehat{B}}{V} \right)}}\left[ Var(\widehat{A})+\frac{1}{{{V}^{2}}}Var(\widehat{B})-\frac{1}{V}Cov(\widehat{A},\widehat{B}) \right] }[/math]


The variances and covariance of [math]\displaystyle{ A }[/math] and [math]\displaystyle{ B }[/math] are estimated from the local Fisher matrix (evaluated at [math]\displaystyle{ \widehat{A} }[/math] , [math]\displaystyle{ \widehat{B}) }[/math] as follows:


[math]\displaystyle{ \left[ \begin{matrix} Var(\widehat{A}) & Cov(\widehat{A},\widehat{B}) \\ Cov(\widehat{B},\widehat{A}) & Var(\widehat{B}) \\ \end{matrix} \right]={{\left[ \begin{matrix} -\tfrac{{{\partial }^{2}}\Lambda }{\partial {{A}^{2}}} & -\tfrac{{{\partial }^{2}}\Lambda }{\partial A\partial B} \\ -\tfrac{{{\partial }^{2}}\Lambda }{\partial B\partial A} & -\tfrac{{{\partial }^{2}}\Lambda }{\partial {{B}^{2}}} \\ \end{matrix} \right]}^{-1}} }[/math]