@MISC{Evans08maximumlikelihood, author = {Addie Andromeda Evans}, title = {Maximum Likelihood Estimation}, year = {2008} }

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Abstract

Estimation Methods Estimation of parameters is a fundamental problem in data analysis. This paper is about maximum likelihood estimation, which is a method that finds the most likely value for the parameter based on the data set collected. A handful of estimation methods existed before maximum likelihood, such as least squares, method of moments and bayesian estimation. This paper will discuss the development of maximum likelihood estimation, the mathematical theory and application of the method, as well as its relationship to other methods of estimation. A basic knowledge of statistics, probability theory and calculus is assumed. Earlier Methods of Estimation Estimation is the process of determining approximate values for parameters of different populations or events. How well the parameter is approximated can depend on the method, the type of data and other factors. Gauss was the first to document the method of least squares, around 1794. This method tests different values of parameters in order to find the best fit model for the given data set. However, least squares is only as robust as the data points are close to the model and thus outliers can cause a least squares estimate to be outside the range of desired accuracy. The method of moments is another way to estimate parameters. The 1st moment is defined to be the mean, and the 2nd moment the variance. The 3rd moment is the skewness and the 4th moment is the kurtosis. In complex models, with more than one parameter, it can be difficult to solve for these moments directly, and so moment generating functions were developed using sophisticated analysis. These moment generating functions can also be used to estimate their respective moments. Bayesian estimation is based on Bayes' Theorem for conditional probability. Bayesian analysis starts with little to no information about the parameter to be estimated. Any data collected can be used to adjust the function of the parameter, thereby improving the estimation of the parameter. This process of refinement can continue as new data is collected until a satisfactory estimate is found.