Arrhenius-Lognormal Model for Interval Data: Difference between revisions

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::<math>e^{\mu'} = e^{\alpha_{0}+ \frac {\alpha_{1}}{T}}\,\!</math>
The above equation is the general log-linear model in ALTA. In ALTA, the coefficients are denoted by <math>\alpha_{i}\,\!</math>. We can see <math>\beta_{0} = \alpha_{0}\,\!</math> and <math>\beta_{1} = \frac{\alpha_{1}}{11605}\,\!</math>.
In fact, the above model also can be expressed using the traditional Arrhenius model:
::<math>e^{\mu'} = e^{\alpha_{0}+ \frac {\alpha_{1}}{T}} = C \times e^{\frac{B}{T}}\,\!</math>
In the book, the following results are provided:
* ML estimations for the model parameters are: <math>\sigma\,\!</math> = 0.52, <math>\beta_{0}\,\!</math> = -10.2, <math>\beta_{1}\,\!</math> = 0.83, (<math>\alpha_{1}\,\!</math> = <math>\beta_{1}\times  \,\!</math> 11605 = 9632.15).
* The standard deviation of each parameter: <math>std(\sigma)\,\!</math>  = 0.06, &nbsp; <math>std(\beta_{0})\,\!</math>  = 1.5, &nbsp;<math>std(\beta_{1})\,\!</math>  = 0.07. Therefore, their variances are: <math>Var(\sigma)\,\!</math>  = 0.0036, &nbsp;<math>Var(\beta_{0})\,\!</math>  = 2.25, &nbsp;<math>Var(\beta_{1})\,\!</math>  = 0.0049. In terms of <math>\alpha_{1}\,\!</math> , the variance is <math>Var(\alpha_{1})\,\!</math>  = 11605<sup>2</sup> &nbsp;and&nbsp; <math>Var(\beta_{1})\,\!</math>  = 659912.5.
* The 95% two-sided confidence bounds are: for <math>\sigma\,\!</math> it is [0.42, 0.64]; for <math>\beta_{0}\,\!</math>  it is [-13.2, -7.2]; for <math>\beta_{1}\,\!</math> it is [0.68, 0.97]. In terms of <math>\alpha_{1}\,\!</math>, the bounds are [7891.14, 11256.85].
* The log-likelihood value is -88.26.
* Common shape parameter test result: the likelihood ratio common shape parameter test statistic is 4.7. It is larger than the critical value 3.84. This indicates that there is some lack of fit in the constant <math>\sigma\,\!</math>  assumption across all the stress levels.




{{Reference_Example_Heading4|ALTA}}
{{Reference_Example_Heading4|ALTA}}

Revision as of 19:44, 11 June 2014

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ALTA_Reference_Examples

This example compares the results for the Arrhenius-Lognormal model with interval data.


Reference Case

The data set is from Example 19.11 on page 508 in the book Statistical Methods for Reliability Data by Dr. Meeker and Dr. Escobar, John Wiley & Sons, 1998.


Data

The data set for a new-technology IC Device is given below.

Number in Group Last Inspected (Hr) State F/S Time to State Temperature (K)
50 788 S 1536 423.15
50 788 S 1536 448.15
50 96 S 96 473.15
1 384 F 788 523.15
3 788 F 1536 523.15
5 1536 F 2304 523.15
41 1536 S 2304 523.15
4 192 F 384 573.15
27 384 F 788 573.15
16 788 F 1536 573.15
3 788 S 1536 573.15


Result

The following function is used for the Ln-Mean [math]\displaystyle{ \mu'\,\! }[/math] of the lognormal distribution:

[math]\displaystyle{ \mu' = \beta_{0}+\beta_{1} \times \frac{11605}{T}\,\! }[/math]


where T is the temperature; [math]\displaystyle{ \beta_{1}\,\! }[/math] is the activation energy; 11605 is calculated from the reciprocal of the Boltzmann constant. This function can be written in the following way:


[math]\displaystyle{ e^{\mu'} = e^{\alpha_{0}+ \frac {\alpha_{1}}{T}}\,\! }[/math]


The above equation is the general log-linear model in ALTA. In ALTA, the coefficients are denoted by [math]\displaystyle{ \alpha_{i}\,\! }[/math]. We can see [math]\displaystyle{ \beta_{0} = \alpha_{0}\,\! }[/math] and [math]\displaystyle{ \beta_{1} = \frac{\alpha_{1}}{11605}\,\! }[/math].

In fact, the above model also can be expressed using the traditional Arrhenius model:

[math]\displaystyle{ e^{\mu'} = e^{\alpha_{0}+ \frac {\alpha_{1}}{T}} = C \times e^{\frac{B}{T}}\,\! }[/math]


In the book, the following results are provided:

  • ML estimations for the model parameters are: [math]\displaystyle{ \sigma\,\! }[/math] = 0.52, [math]\displaystyle{ \beta_{0}\,\! }[/math] = -10.2, [math]\displaystyle{ \beta_{1}\,\! }[/math] = 0.83, ([math]\displaystyle{ \alpha_{1}\,\! }[/math] = [math]\displaystyle{ \beta_{1}\times \,\! }[/math] 11605 = 9632.15).
  • The standard deviation of each parameter: [math]\displaystyle{ std(\sigma)\,\! }[/math] = 0.06,   [math]\displaystyle{ std(\beta_{0})\,\! }[/math] = 1.5,  [math]\displaystyle{ std(\beta_{1})\,\! }[/math] = 0.07. Therefore, their variances are: [math]\displaystyle{ Var(\sigma)\,\! }[/math] = 0.0036,  [math]\displaystyle{ Var(\beta_{0})\,\! }[/math] = 2.25,  [math]\displaystyle{ Var(\beta_{1})\,\! }[/math] = 0.0049. In terms of [math]\displaystyle{ \alpha_{1}\,\! }[/math] , the variance is [math]\displaystyle{ Var(\alpha_{1})\,\! }[/math] = 116052  and  [math]\displaystyle{ Var(\beta_{1})\,\! }[/math] = 659912.5.
  • The 95% two-sided confidence bounds are: for [math]\displaystyle{ \sigma\,\! }[/math] it is [0.42, 0.64]; for [math]\displaystyle{ \beta_{0}\,\! }[/math] it is [-13.2, -7.2]; for [math]\displaystyle{ \beta_{1}\,\! }[/math] it is [0.68, 0.97]. In terms of [math]\displaystyle{ \alpha_{1}\,\! }[/math], the bounds are [7891.14, 11256.85].
  • The log-likelihood value is -88.26.
  • Common shape parameter test result: the likelihood ratio common shape parameter test statistic is 4.7. It is larger than the critical value 3.84. This indicates that there is some lack of fit in the constant [math]\displaystyle{ \sigma\,\! }[/math] assumption across all the stress levels.


Results in ALTA

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