What is the likelihood function and what are the methods of estimation discussed in statistics?
Understand the Problem
The question is about the concept of likelihood functions and methods of estimation in statistics, focusing on various techniques such as the method of maximum likelihood and method of moments. It discusses the properties of good estimators and outlines some statistical methods.
Answer
Likelihood function: measures how well data match the model. Estimation methods: method of moments, MLE, least squares, minimum variance, minimum chi-square, and inverse probability.
The likelihood function is a measure of how well a statistical model explains observed data by calculating the probability of seeing that data under different parameter values of the model. The main methods of estimation discussed are method of moments, maximum likelihood estimation (MLE), least squares, minimum variance, minimum chi-square, and method of inverse probability.
Answer for screen readers
The likelihood function is a measure of how well a statistical model explains observed data by calculating the probability of seeing that data under different parameter values of the model. The main methods of estimation discussed are method of moments, maximum likelihood estimation (MLE), least squares, minimum variance, minimum chi-square, and method of inverse probability.
More Information
Estimation methods are essential for statistical analysis as they allow for the calculation and inference of population parameters based on sample data.
Tips
A common mistake is confusing the likelihood function with probability density function. The likelihood function considers the data fixed and varies the parameters.
Sources
- Likelihood function - en.wikipedia.org
- Likelihood Function: Overview / Simple Definition - statisticshowto.com
- Beginner's Guide To Maximum Likelihood Estimation - aptech.com
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