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 | Determine the lognormal parameter estimates for the data given in the table below. | ||
{|align="center" border= | {|border="1" align="center" style="border-collapse: collapse;" cellpadding="5" cellspacing="5" | ||
|- | |- | ||
|colspan="3" style="text-align:center"| | |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> | ||
Line 43: | Line 42: | ||
& {{{\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]