Politecnico di Milano
Question
Asked 12th Jan, 2021
Why shape parameter in Weibull distribution difficult to model or estimate?
In estimation Weibull parameter using parameter prediction or parameter recovery, shape parameter is relatively difficult to model than scale parameter. What is the reason behind it?
Most recent answer
For estimating Weibull parameters you can use:
1) moment method (already suggested by Prof. Tiryakioglu) where you take the log(data) and then calculated moments (the data will be in this way transformed to SEVD);
2) use the ML method: excellent books by Nelson explain it.
It is worth plotting the data onto a Weibull probability paper (once again log(data)): the modulus is the slope of the data.
2 Recommendations
All Answers (4)
University of Auckland
Because scale is simply the mean, calculated using simple mathematical functions, but shape describes kurtosis
1 Recommendation
Jacksonville University
Weibull modulus, aka the shape parameter, is not difficult to estimate. There are simple techniques to estimate it along with the scale parameter, such as the least squares and method of moments.
1 Recommendation
Technion - Israel Institute of Technology
I strongly recommend you to use SW such as JMP (for example) for calculation Weibull distribution parameters and it'll take you a couple of minutes to get the distribution parameters
1 Recommendation
Politecnico di Milano
For estimating Weibull parameters you can use:
1) moment method (already suggested by Prof. Tiryakioglu) where you take the log(data) and then calculated moments (the data will be in this way transformed to SEVD);
2) use the ML method: excellent books by Nelson explain it.
It is worth plotting the data onto a Weibull probability paper (once again log(data)): the modulus is the slope of the data.
2 Recommendations
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