Maximum Likelihood Estimator Question

In summary, we are given that the lifetimes of components are Gamma distributed with shape parameter a = 3 and scale parameter λ. The probability density function for this distribution is f(x) = (λ^a).x^(a-1).e^(-λx)/Γ(a). To obtain the maximum likelihood estimate of λ, we set the derivative of the log-likelihood function (l) equal to 0, which results in λ = 6/x. This makes sense as it represents the maximum likelihood estimate for λ given that a = 3.
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Homework Statement


Lifetimes of components are Gamma distributed. The parameters of the Gamma are
shape = a
scale = λ

The pdf is:

f(x) = (λ^a).x^(a-1).e^(-λx)/Γ(a)

In this case, it is known that a = 3. Obtain the MLE of λ.


Homework Equations




The Attempt at a Solution


Hi everyone,

Here's what I've done so far. To me it makes sense but if you could please take a look at it and see if I've gone wrong somewhere? It just seems a bit too straightforward...

--

L(λ;x) = f(x) ... no product as there are no x_i's, only one parameter, x

Sub in a=3 to eqn

L = (1/2).(λ^3).(x^2).(e^(-λx))/2 ... as Γ(3) = (3-1)! = 2! = 2

l = ln L = ln((x^2)/2) + 3.ln(λ^2) - λx ... where l is a lower-case L

∂l/∂λ = 0 + 6/λ - x
= 6/λ - x

Let ∂l/∂λ = 0 to find maximum


6/λ - x = 0

λ = 6/x


Can I leave it at that? Was I right to omit the x_i's from the picture? Thanks in advance for any help!
 
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  • #2


Hi there,

Your approach seems correct to me. Since you are looking for the MLE of λ, you don't need to include the x_i's in the picture as they are just observed values and not parameters. Your final result of λ = 6/x also makes sense, as it represents the maximum likelihood estimate for the scale parameter given that the shape parameter is known to be 3. Good job!
 

1. What is a maximum likelihood estimator (MLE)?

A maximum likelihood estimator is a statistical method used to estimate the parameters of a probability distribution by maximizing the likelihood function. It is commonly used in data analysis and machine learning to find the most likely values for unknown parameters based on the observed data.

2. How does the MLE method work?

The MLE method works by finding the values of the unknown parameters that maximize the likelihood function, which is a measure of how likely the observed data is to occur given a particular set of parameter values. This is typically done using mathematical optimization techniques such as gradient descent or the Newton-Raphson method.

3. What are the assumptions of MLE?

The main assumptions of MLE are that the data follows a specific probability distribution and that the observations are independent and identically distributed. Additionally, the data should be continuous and the sample size should be large enough for the central limit theorem to apply.

4. How is MLE different from other estimation methods?

Unlike other estimation methods, MLE does not require any prior knowledge or assumptions about the distribution of the data. It also provides a measure of uncertainty through confidence intervals for the estimated parameters. However, it can be computationally intensive and may not always provide the best estimates in small sample sizes.

5. What are the applications of MLE?

MLE has a wide range of applications in various fields such as finance, economics, biology, and engineering. It is commonly used for parameter estimation in regression models, time series analysis, and survival analysis. It is also used in machine learning algorithms such as logistic regression and neural networks.

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