Loss Models: From Data to Decisions, 4th Edition

13.2 Maximum likelihood estimation

13.2.1 Introduction

Estimation by the method of moments and percentile matching is often easy to do, but these estimators tend to perform poorly mainly because they use a few features of the data, rather than the entire set of observations. It is particularly important to use as much information as possible when the population has a heavy right tail. For example, when estimating parameters for the normal distribution, the sample mean and variance are sufficient.³ However, when estimating parameters for a Pareto distribution, it is important to know all the extreme observations in order to successfully estimate α. Another drawback of these methods is that they require that all the observations are from the same random variable. Otherwise, it is not clear what to use for the population moments or percentiles. For example, if half the observations have a deductible of 50 and half have a deductible of 100, it is not clear to what the sample mean should be equated.⁴ Finally, these methods allow the analyst to make arbitrary decisions regarding the moments or percentiles to use.

There are a variety of estimators that use the individual data points. All of them are implemented by setting an objective function and then determining the parameter values that optimize that function. For example, we could estimate parameters by minimizing the maximum difference between the distribution function for the parametric model and the distribution function as determined ...