Gamma and Weibull location parameter estimation

In summary, a Computer Science student is using the kolmogorov-Smirnov test to detect the best probability distribution for their data. They are attempting to add the three-parameter Weibull and gamma distributions but are having trouble finding a direct method to estimate the location parameter. They are seeking help and have been recommended to use Matlab's Statistical toolkit for estimators.
  • #1
monicamlmc
1
0
Hi all,

I have a set of samples and I would like to detect the probability distribution that best represents the data. I'm using the kolmogorov-Smirnov test to verify the goodness-of-fit for some well-known distributions, like Gamma, exponential and Weibull. Since I don't know the distribution parameters, I'm estimating them (using the mechanism of rank regression on Y in most cases).

My problem is that I need to extend my set of tested distributions adding the three-parameter weibull and three-parameter gamma distributions. However, I can't find a "direct" method to estimate the location parameter for both distributions. By "direct" I mean some closed formula. I found some iterative methods, but I'm trying to avoid them because speed of detection is a very important factor in my work. Btw, I'm a Computer Science student, I have a very limited background in statistics... :-( may be what I want to do is not possible, I don't know...

Can anyone help me?

Thanks in advance!
 
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  • #2
Matlab's Statistical toolkit has estimators for these distributions. I've used the Weibull one, can't remember if it does all three parameters. You input the raw data and it gives you parameter fits.
 
  • #3


Hello,

Thank you for sharing your question and background information. Estimating the location parameter for the three-parameter Weibull and Gamma distributions can be done using the method of moments or maximum likelihood estimation. However, these methods do involve some iterative calculations, so they may not be the most efficient for your purposes.

Another approach you can consider is using the method of moments with the rank regression estimator you mentioned. This involves using the sample mean and variance to estimate the shape and scale parameters, and then using the rank regression estimator to estimate the location parameter. This method may be more efficient for your needs as it does not involve iterative calculations.

I hope this helps and good luck with your analysis!
 

Related to Gamma and Weibull location parameter estimation

What is the purpose of estimating the location parameter for Gamma and Weibull distributions?

The location parameter represents the location or shift of the distribution on the x-axis. Estimating this parameter allows us to determine the central tendency of the data and make comparisons between different data sets.

What is the difference between the location parameter for Gamma and Weibull distributions?

The location parameter for Gamma distributions is typically denoted by μ and represents the expected value of the distribution. For Weibull distributions, the location parameter is typically denoted by γ and represents the value of the distribution at which the probability density function is highest.

How are the location parameters estimated for Gamma and Weibull distributions?

The most common method for estimating the location parameter is by using the method of moments, which involves equating the sample mean to the theoretical mean of the distribution. Other methods include maximum likelihood estimation and Bayesian estimation.

What are some common challenges in estimating the location parameter for Gamma and Weibull distributions?

One of the main challenges is the presence of outliers in the data, which can heavily influence the estimated location parameter. Additionally, the choice of estimation method and the sample size can also impact the accuracy of the estimated parameter.

Can the location parameter be negative for Gamma and Weibull distributions?

No, the location parameter for both distributions must be positive as it represents the shift or location of the distribution on the x-axis. If negative values are obtained, it is likely due to a mistake in the estimation process.

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