Finding the mle for the gamma distribution

DerivativesIn summary, the conversation was about taking the natural log of a given parameter \theta and finding its derivative using the gamma function. The person was stuck because they did not know how to find the derivative of a factorial, but another person suggested using the polygamma function instead.
  • #1
Artusartos
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So if the parameter [tex]\theta[/tex] is alpha...

[tex]L(\theta) = \frac{1}{\Gamma(\theta)\beta^{\theta}} x^{\theta-1} e^{-x/\beta}[/tex]

Now I take the natural log of that...


[tex]ln(L(\theta)) = ln(\frac{1}{(1-\theta)!}) + ln(\frac{1}{\beta^{\theta}}) + ln(x^{\theta-1}) + ln(e^{-x/\beta})[/tex]

Now I want to take the derivative of this...but I'm stuck because I don't know what the derivative of [tex]\frac{1}{(1-\theta)!}[/tex] is...how can I find the derivative of a factorial? :confused:
 
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  • #2
Why did you replace the gamma function with the factorial in the first place? Differentiate the gamma function, problem solved.
 
  • #3

FAQ: Finding the mle for the gamma distribution

What is the gamma distribution?

The gamma distribution is a continuous probability distribution that is commonly used to model positive-valued data, such as reaction times, waiting times, and insurance claims. It is characterized by two parameters, shape and scale, and is often used in statistical analysis and modeling.

How is the maximum likelihood estimator (MLE) calculated for the gamma distribution?

The MLE for the gamma distribution is calculated by finding the values of the shape and scale parameters that maximize the likelihood function, which is a measure of how likely a given set of data is to occur under a specific distribution. This can be done using numerical optimization methods or by solving the equations for the first derivatives of the likelihood function with respect to the parameters.

What is the importance of finding the MLE for the gamma distribution?

Finding the MLE for the gamma distribution is important because it allows us to estimate the parameters of the distribution from a given set of data. This, in turn, allows us to make inferences and predictions about the population from which the data was collected. The MLE is also a desirable estimator because it has many desirable statistical properties.

Can the MLE for the gamma distribution be calculated analytically?

In some cases, the MLE for the gamma distribution can be calculated analytically, meaning that it can be expressed in closed form using mathematical equations. However, in many cases, it needs to be calculated numerically using computational methods due to the complexity of the likelihood function.

Are there any limitations or assumptions when using the MLE for the gamma distribution?

Like any statistical method, there are limitations and assumptions when using the MLE for the gamma distribution. One major assumption is that the data being analyzed follows a gamma distribution. Additionally, the MLE may not be appropriate for small sample sizes or when the data contains outliers or influential points. It is important to assess the validity of these assumptions before using the MLE for the gamma distribution.

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