Maximum-Likelihood Estimation

The likelihood function provides the basis for a general parameter estimation approach known as maximum likelihood estimation. This is the most popular parameter estimation approach because of its excellent large sample properties. It yields good estimators in cases where minimum variance unbiased estimators are not available. However, the approach requires a set of data with known pdf. This chapter covers maximum likelihood estimation, its properties, and its relation to BLUE and least-squares estimators. It also covers maximum a priori estimation, where the distribution of the unknown parameters is known. It also presents the related maximum a posteriori estimator which requires prior knowledge of the distribution of the estimated parameter.