Loglogistic Distribution Example: Difference between revisions

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Determine the loglogistic parameter estimates for the data given in the following table.  
Determine the loglogistic parameter estimates for the data given in the following table.  


<center><math>\overset{{}}{\mathop{\text{Test data}}}\,</math></center>
<center><math>\overset{{}}{\mathop{\text{Test data}}}\,\,\!</math></center>


<center><math>\begin{matrix}
<center><math>\begin{matrix}
Line 18: Line 18:
   \text{9} & \text{675} & \text{680}  \\
   \text{9} & \text{675} & \text{680}  \\
   \text{10} & \text{884} & \text{889}  \\
   \text{10} & \text{884} & \text{889}  \\
\end{matrix}</math></center>
\end{matrix}\,\!</math></center>




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   & {{{\hat{\mu }}}^{\prime }}= & 5.9772 \\  
   & {{{\hat{\mu }}}^{\prime }}= & 5.9772 \\  
  & {{{\hat{\sigma }}}_{{{T}'}}}= & 0.3256   
  & {{{\hat{\sigma }}}_{{{T}'}}}= & 0.3256   
\end{align}</math>
\end{align}\,\!</math>


For rank regression on <math>X\ </math>:   
For rank regression on <math>X\,\!</math>:   


::<math>\begin{align}
::<math>\begin{align}
   & \hat{\mu }= & 5.9281 \\  
   & \hat{\mu }= & 5.9281 \\  
  & \hat{\sigma }= & 0.3821   
  & \hat{\sigma }= & 0.3821   
\end{align}</math>
\end{align}\,\!</math>


For rank regression on <math>Y\ </math>:   
For rank regression on <math>Y\,\!</math>:   


::<math>\begin{align}
::<math>\begin{align}
   & \hat{\mu }= & 5.9772 \\  
   & \hat{\mu }= & 5.9772 \\  
  & \hat{\sigma }= & 0.3256   
  & \hat{\sigma }= & 0.3256   
\end{align}</math>
\end{align}\,\!</math>

Revision as of 17:51, 26 September 2012

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This example appears in the Life Data Analysis Reference book.

Determine the loglogistic parameter estimates for the data given in the following table.

[math]\displaystyle{ \overset{{}}{\mathop{\text{Test data}}}\,\,\! }[/math]
[math]\displaystyle{ \begin{matrix} \text{Data point index} & \text{Last Inspected} & \text{State End time} \\ \text{1} & \text{105} & \text{106} \\ \text{2} & \text{197} & \text{200} \\ \text{3} & \text{297} & \text{301} \\ \text{4} & \text{330} & \text{335} \\ \text{5} & \text{393} & \text{401} \\ \text{6} & \text{423} & \text{426} \\ \text{7} & \text{460} & \text{468} \\ \text{8} & \text{569} & \text{570} \\ \text{9} & \text{675} & \text{680} \\ \text{10} & \text{884} & \text{889} \\ \end{matrix}\,\! }[/math]


Set up the folio for times-to-failure data that includes interval and left censored data, then enter the data. The computed parameters for maximum likelihood are calculated to be:

[math]\displaystyle{ \begin{align} & {{{\hat{\mu }}}^{\prime }}= & 5.9772 \\ & {{{\hat{\sigma }}}_{{{T}'}}}= & 0.3256 \end{align}\,\! }[/math]

For rank regression on [math]\displaystyle{ X\,\! }[/math]:

[math]\displaystyle{ \begin{align} & \hat{\mu }= & 5.9281 \\ & \hat{\sigma }= & 0.3821 \end{align}\,\! }[/math]

For rank regression on [math]\displaystyle{ Y\,\! }[/math]:

[math]\displaystyle{ \begin{align} & \hat{\mu }= & 5.9772 \\ & \hat{\sigma }= & 0.3256 \end{align}\,\! }[/math]