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# Maximum Likelihood Estimation

Module - 6 Statistical Inference
Maximum Likelihood Estimation

Overview

Maximum likelihood estimation is a widely used statistical method for estimating the parameters of a probability distribution. It involves finding the values of the parameters that maximize the likelihood function, which is a measure of how well the distribution fits the data. The method has many applications in various fields such as finance, engineering, and medicine.

Introduction to Maximum Likelihood Estimation

Maximum likelihood estimation (MLE) is a statistical method for estimating the parameters of a probability distribution based on a sample of data. The method is based on the likelihood function, which measures how likely the observed data is for different values of the parameters.

MLE is widely used in many fields of research, including biology, economics, finance, engineering, and social sciences. It provides a powerful and flexible tool for modeling and analyzing complex data sets.

Understanding the Likelihood Function

The likelihood function is defined as the probability of observing the data, given a set of parameter values. It is calculated by taking the product of the probability density function (PDF) or probability mass function (PMF) for each observation in the sample.

For example, let's say we have a sample of n independent and identically distributed (i.i.d) random variables X1, X2, ..., Xn, which follow a normal distribution with mean mu and variance sigma^2. The likelihood function for this sample can be written as:

L(mu, sigma^2 | x1, x2, ..., xn) = f(x1 | mu, sigma^2) * f(x2 | mu, sigma^2) * ... * f(xn | mu, sigma^2)


where f(xi | mu, sigma^2) is the PDF of the normal distribution with mean mu and variance sigma^2.

Maximizing the Likelihood Function

The goal of MLE is to find the values of the parameters that maximize the likelihood function. This is done by taking the derivative of the log-likelihood function with respect to the parameters and setting it equal to zero.

The log-likelihood function is used instead of the likelihood function because it simplifies the calculations and avoids issues with numerical precision. It is defined as:

l(mu, sigma^2 | x1, x2, ..., xn) = log(L(mu, sigma^2 | x1, x2, ..., xn))


Taking the partial derivatives of the log-likelihood function with respect to mu and sigma^2, and setting them equal to zero, we obtain the maximum likelihood estimates (MLEs) of mu and sigma^2:

mu_hat = (1/n) * sum(xi)
sigma_hat^2 = (1/n) * sum(xi - mu_hat)^2


where xi is the ith observation in the sample.

Properties of Maximum Likelihood Estimators

MLEs have several desirable properties, including consistency, efficiency, and asymptotic normality.

Consistency: As the sample size n increases, the MLEs converge to the true values of the parameters.

Efficiency: Among all unbiased estimators, MLEs have the lowest variance, which means they are the most efficient.

Asymptotic normality: Under certain regularity conditions, the MLEs follow a normal distribution with mean equal to the true parameter value and variance equal to the inverse of the Fisher information matrix.

Applications of Maximum Likelihood Estimation

MLE has a wide range of applications, including:

1. Regression analysis: MLE is used to estimate the coefficients of linear regression models.
2. Survival analysis: MLE is used to estimate the parameters of survival distributions, such as the Weibull and exponential distributions.
3. Machine learning: MLE is used in many machine learning algorithms, such as logistic regression, neural networks, and hidden Markov models.
4. Phylogenetics: MLE is used to estimate the parameters of evolutionary models in biology, such as the substitution rates and branch lengths in phylogenetic trees.

Limitations of Maximum Likelihood Estimation

While Maximum Likelihood Estimation is a powerful and widely used method for estimating parameters, there are some limitations to be aware of. Here are some of the main limitations:

1. Sensitivity to Outliers: MLE is highly sensitive to outliers in the data, and can produce biased estimates when extreme values are present. In some cases, robust methods may be needed to handle outliers.
2. Model Misspecification: MLE assumes that the model used to generate the data is correct. If the model is misspecified, the estimates produced by MLE may be biased or inconsistent. Therefore, it is important to carefully choose the appropriate model for the data.
3. Sample Size: MLE requires a sufficiently large sample size to obtain reliable estimates. As sample size decreases, the variance of the estimator increases, which can lead to less accurate estimates.
4. Multiple Modes: MLE can be affected by multiple modes in the likelihood function, which can lead to non-converging estimates or estimates that are highly dependent on the initial conditions.
5. Computational Complexity: In some cases, MLE may require computationally intensive numerical methods to obtain the estimates. This can be a challenge when dealing with large datasets or complex models.

It is important to be aware of these limitations when using MLE and to carefully consider whether it is the appropriate method for a given problem.

Key Takeaways

1. Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a statistical model based on the likelihood function. The likelihood function is used to find the set of parameters that maximize the probability of observing the given data.
2. MLE is a powerful tool for estimating parameters in complex models, but it has some limitations as well. For example, it assumes that the data is independently and identically distributed, which may not always be the case in real-world scenarios.
3. The process of maximum likelihood estimation involves finding the value of the parameters that maximize the likelihood function by taking the first derivative of the likelihood function with respect to each parameter and equating it to zero.
4. MLE can be applied to both discrete and continuous distributions. MLE is a useful tool for parameter estimation and can be used to make predictions and draw inferences about the data.
5. Overall, Maximum Likelihood Estimation is a powerful and widely used method for estimating the parameters of a statistical model based on the likelihood function. It has many advantages, such as unbiasedness and efficiency, but also has some limitations, such as assumptions of independence and identically distributed data.

Conclusion

Maximum likelihood estimation is a powerful method for estimating the parameters of a probability distribution based on observed data. It involves finding the parameter values that maximize the likelihood function, which is a measure of how well the distribution fits the data. Maximum likelihood estimation has many applications in statistics, machine learning, and other fields, and it is an important tool for understanding and analyzing data.

Quiz

1. What is Maximum Likelihood Estimation (MLE)?

A. A statistical method for estimating parameters of a probability distribution

B. A method for hypothesis testing

C. A method for calculating the mean of a dataset

D. A method for calculating the variance of a dataset

Ans: A

2. What is the likelihood function in MLE?

A. A measure of how well the data fits the distribution

B. A measure of how well the distribution fits the data

C. A measure of the variance of the data

D. A measure of the mean of the data

Ans: A

3. What is the main goal of MLE?

A. To find the values of the parameters that maximize the likelihood function

B. To find the values of the parameters that minimize the likelihood function

C. To find the values of the parameters that maximize the variance of the data

D. To find the values of the parameters that minimize the mean of the data

Ans: A

4. Which of the following fields does not use MLE?

A. Finance

B. Engineering

C. Medicine

D. Political Science

Ans: D

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