Question on maximum likelihood

In summary, maximum likelihood estimation is a statistical method used to estimate the parameters of a probability distribution by maximizing the likelihood function. It works by finding the values of the parameters that make the observed data the most likely to occur. The main difference between maximum likelihood and least squares is that maximum likelihood is used for estimating parameters of a probability distribution, while least squares is used for finding the best fit line for a set of data points. The assumptions of maximum likelihood estimation include a specified probability distribution, independent and identically distributed data, and a sufficient amount of data to accurately estimate the parameters. Some advantages of using maximum likelihood estimation include its ability to handle complex models, its robustness to small deviations from the assumed distribution, and its ability to provide confidence
  • #1
Simfish
Gold Member
823
2
http://en.wikipedia.org/wiki/Maximum_likelihood


What exactly does the "arg" here mean? It seems to be an unnecessary - the max L(\theta) seems to be sufficient enough. Or am I missing something?
[tex]\widehat{\theta} = \underset{\theta}{\operatorname{arg\ max}}\ \mathcal{L}(\theta).[/tex]
 
Physics news on Phys.org
  • #2
arg is needed because the left-hand side is the argument of L.

One can write [tex]L(\widehat{\theta}) = \underset{\theta}\max\ {L(\theta)}.[/tex].

Or one can write [tex]\widehat{\theta} = \underset{\theta}{\operatorname{arg\ max}}\ {L}(\theta).[/tex]
 
  • #3


The "arg" in this context stands for "argument" and it is used to indicate that the maximum likelihood estimate is the value of the parameter, \theta, that maximizes the likelihood function, \mathcal{L}(\theta). In other words, it is the argument that gives the maximum value of the likelihood function. This notation is commonly used in mathematical and statistical contexts to indicate the input or variable that results in a maximum or minimum value of a function. It is not necessary, but it helps to clarify the purpose of the notation and the meaning of the estimate.
 

1. What is maximum likelihood estimation?

Maximum likelihood estimation is a statistical method used to estimate the parameters of a probability distribution by maximizing the likelihood function. It is commonly used in statistical modeling to find the best fit for a given set of data.

2. How does maximum likelihood estimation work?

Maximum likelihood estimation works by finding the values of the parameters that make the observed data the most likely to occur. This is done by calculating the likelihood function, which is the probability of obtaining the observed data given the parameter values, and then finding the values that maximize this function.

3. What is the difference between maximum likelihood and least squares?

The main difference between maximum likelihood and least squares is that maximum likelihood is used for estimating parameters of a probability distribution, while least squares is used for finding the best fit line for a set of data points. Maximum likelihood also takes into account the variability of the data, while least squares does not.

4. What are the assumptions of maximum likelihood estimation?

The assumptions of maximum likelihood estimation include a specified probability distribution, independent and identically distributed data, and a sufficient amount of data to accurately estimate the parameters. Additionally, the data should not be heavily skewed or have extreme outliers.

5. What are the advantages of using maximum likelihood estimation?

Some advantages of using maximum likelihood estimation include its ability to handle complex models, its robustness to small deviations from the assumed distribution, and its ability to provide confidence intervals for the estimated parameters. It is also a widely used and well-established method in statistical analysis.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
16
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
952
  • Set Theory, Logic, Probability, Statistics
Replies
8
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
12
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
901
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
2K
Back
Top