Narrow Results

One of the most important objectives of statistical inference is to estimate unknown model parameters based on an observed data. In this chapter, we will introduce some fundamental estimation methods for distributional model parameters. In particular, the maximum likelihood estimation and the method of moments estimation/generalized method of moments estimation are discussed, and their asymptotic properties investigated. Then we will discuss the methods for evaluating parameter estimators using the mean squared error criterion. The Lagrange multiplier method and the Cramer-Rao lower bound are used to derive the best unbiased estimators.