Maximum Likelihood Estimation

Definition

Maximum likelihood estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution by maximizing the likelihood function. It is a fundamental concept in statistical inference that helps determine the values of unknown parameters that best explain the observed data.

5 Must Know Facts For Your Next Test

1. Maximum likelihood estimation is a widely used method for parameter estimation in both discrete and continuous probability distributions.
2. In the context of the discrete distribution (e.g., the playing card experiment), MLE can be used to estimate the probabilities of each outcome (e.g., the probability of drawing a particular card from a deck).
3. For the exponential distribution, MLE can be used to estimate the rate parameter, which determines the shape of the distribution and the average time between events.
4. MLE is based on the principle of finding the parameter values that make the observed data most likely to have occurred, given the assumed probability distribution.
5. The MLE method is known to have desirable statistical properties, such as consistency, asymptotic normality, and efficiency, under certain conditions.

Review Questions

• Explain how maximum likelihood estimation can be used to estimate the probabilities of each outcome in a discrete probability distribution, such as the playing card experiment.
  • In the context of the discrete distribution (e.g., the playing card experiment), maximum likelihood estimation (MLE) can be used to estimate the probabilities of each outcome, such as the probability of drawing a particular card from a deck. The MLE method involves finding the parameter values (in this case, the probabilities of each card) that maximize the likelihood of observing the given data (the specific cards drawn). By maximizing the likelihood function, the MLE approach determines the parameter values that make the observed data most likely to have occurred, given the assumed discrete probability distribution.
• Describe how maximum likelihood estimation can be used to estimate the rate parameter of the exponential distribution.
  • For the exponential distribution, maximum likelihood estimation (MLE) can be used to estimate the rate parameter, which determines the shape of the distribution and the average time between events. The MLE method involves finding the value of the rate parameter that maximizes the likelihood of observing the given data (the observed event times or inter-arrival times). By maximizing the likelihood function, the MLE approach determines the rate parameter value that makes the observed data most likely to have occurred, given the assumed exponential probability distribution. This allows for accurate estimation of the average time between events or the intensity of the underlying process.
• Explain the key statistical properties of maximum likelihood estimation that make it a desirable method for parameter estimation.
  • Maximum likelihood estimation (MLE) is known to have several desirable statistical properties that make it a widely used method for parameter estimation. Firstly, MLE is consistent, meaning that as the sample size increases, the MLE estimates converge to the true parameter values. Secondly, MLE is asymptotically normal, which means that the sampling distribution of the MLE estimates approaches a normal distribution as the sample size grows. Finally, MLE is efficient, meaning that the MLE estimates have the smallest possible variance among all unbiased estimators. These properties ensure that MLE provides accurate and reliable parameter estimates, making it a preferred choice for statistical inference in both discrete and continuous probability distributions.

Related terms

Likelihood Function:

The likelihood function is a function that expresses the probability or likelihood of the observed data given the model parameters. The goal of MLE is to find the parameter values that maximize this likelihood function.

Parameter Estimation: Parameter estimation is the process of using sample data to calculate the values of the unknown parameters of a statistical model, such as the mean and standard deviation of a normal distribution.

Probability Distribution:

A probability distribution is a mathematical function that describes the possible values and their associated probabilities for a random variable. MLE is used to estimate the parameters of these distributions, such as the rate parameter in the exponential distribution.