Template:Example: Lognormal General Example Interval Data: Difference between revisions

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'''Lognormal Distribution General Example Interval Data'''
'''Lognormal Distribution General Example Interval Data'''


Determine the lognormal parameter estimates for the data given in Table below.
Determine the lognormal parameter estimates for the data given in the table below.
{|align="center" border=1 cellspacing=1
{|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5"
|-
|-
|colspan="3" style="text-align:center"| Table - Non-Grouped Data Times-to-Failure with intervals (lnterval and left censored)
|colspan="3" style="text-align:center"| Non-Grouped Data Times-to-Failure with intervals (lnterval and left censored)
|-  
|-  
!Data point index
!Data point index
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  & {{{\hat{\sigma' }}}}= & 0.18   
  & {{{\hat{\sigma' }}}}= & 0.18   
\end{align}</math>
\end{align}</math>


For rank regression on  <math>X\ \ :</math>   
For rank regression on  <math>X\ \ :</math>   
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  & {{{\hat{\sigma' }}}}= & 0.17   
  & {{{\hat{\sigma' }}}}= & 0.17   
\end{align}</math>
\end{align}</math>


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

Revision as of 04:50, 8 August 2012

Lognormal Distribution General Example Interval Data

Determine the lognormal parameter estimates for the data given in the table below.

Non-Grouped Data Times-to-Failure with intervals (lnterval and left censored)
Data point index Last Inspected State End Time
1 30 32
2 32 35
3 35 37
4 37 40
5 42 42
6 45 45
7 50 50
8 55 55

Solution

This is a sequence of interval times-to-failure where the intervals vary substantially in length. Using Weibull++, the computed parameters for maximum likelihood are calculated to be:

[math]\displaystyle{ \begin{align} & {{{\hat{\mu }}}^{\prime }}= & 3.64 \\ & {{{\hat{\sigma' }}}}= & 0.18 \end{align} }[/math]

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

[math]\displaystyle{ \begin{align} & {{{\hat{\mu }}}^{\prime }}= & 3.64 \\ & {{{\hat{\sigma' }}}}= & 0.17 \end{align} }[/math]

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

[math]\displaystyle{ \begin{align} & {{{\hat{\mu }}}^{\prime }}= & 3.64 \\ & {{{\hat{\sigma' }}}}= & 0.21 \end{align} }[/math]