What is the maximum likelihood estimator for a given density function?

In summary, the conversation discusses estimating the value of a in the function f(x)=ax^(a-1) using maximum likelihood. The likelihood function for an i.i.d. sample is given and it is mentioned that simplifying a product of different bases, each raised to the same power, can help in finding the value of a.
  • #1
sara_87
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0

Homework Statement



pdf: f(x)=ax^(a-1) ; 0<x<1, a>0
estimate a by maximum likelihood

Homework Equations


let L be maximum likelihood
L=(a(x[1])^(a-1))(a(x[2])^(a-1))...(a(x[n])^(a-1))

The Attempt at a Solution



Im trying to make this into a nicer expression:
L=a^n... (now I am stuck)

Any help would be v much appreciated.
Thank you.
 
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  • #2
Remember that if your density is [tex] f(x) [/tex], then the likelihood function for an i.i.d. sample is

[tex]
L(x_1, \dots, x_n) = \prod_{i=1}^n f(x_i)
[/tex]

For the density you give this is

[tex]
L(x_1, \dots, x_n) = \prod_{i=1}^n a x_i^{a-1} = a^n \prod_{i=1}^n x_i^{a-1}
[/tex]

What do you know about simplifying a product of different bases when each is raised to the same power?
 

1. What is maximum likelihood error?

Maximum likelihood error is a statistical method used to estimate the most likely values of the parameters in a given model based on observed data. It is a commonly used approach in machine learning and can be used to make predictions or classify data.

2. How is maximum likelihood error calculated?

The maximum likelihood error is calculated by finding the values of the model parameters that maximize the likelihood of the observed data. This is typically done by taking the derivative of the likelihood function with respect to the model parameters and setting it equal to zero.

3. What is the difference between maximum likelihood error and other error metrics?

Maximum likelihood error takes into account the likelihood of the observed data given the model parameters, while other error metrics such as mean squared error or absolute error only consider the difference between predicted and observed values. This makes maximum likelihood error a more comprehensive measure of model performance.

4. What are the assumptions of maximum likelihood error?

The most common assumption of maximum likelihood error is that the data follows a specific probability distribution, such as Gaussian or Poisson. Additionally, it assumes that the observations are independent and that the model is correctly specified.

5. Can maximum likelihood error be used for any type of data?

Maximum likelihood error can be used for a wide range of data types, including continuous, discrete, and categorical data. However, it is important to ensure that the underlying assumptions are met before using this method for a specific dataset.

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