Maximum likelihood estimation  (Page 4/3)

The maximum likelihood estimator (MLE) is an alternative to the minimum variance unbiased estimator (MVUE).For many estimation problems, the MVUE does not exist. Moreover, when it does exist, there is no systematic procedure forfinding it. In constrast, the MLE does not necessarily satisfy any optimality criterion, but it can almost always be computed,either through exact formulas or numerical techniques. For this reason, the MLE is one of the most common estimation procedures used in practice.

The MLE is an important type of estimator for the following reasons:

• The MLE implements the likelihood principle.
• MLEs are often simple and easy to compute.
• MLEs have asymptotic optimality properties (consistency and efficiency).
• MLEs are invariant under reparameterization.
• If an efficient estimator exists, it is the MLE.
• In signal detection with unknown parameters (composite hypothesis testing), MLEs are used in implementing thegeneralized likelihood ratio test (GLRT).

The likelihood principle

Supposed the data $X$ is distributed according to the density or mass function $p(, x)$ . The likelihood function for $$ is defined by $$l(x, )\equiv p(, x)$$ At first glance, the likelihood function is nothing new - it is simply a way of rewriting the pdf/pmf of $X$ . The difference between the likelihood and the pdf or pmf is what is held fixed and whatis allowed to vary. When we talk about the likelihood, we view the observation $x$ as being fixed, and the parameter $$ as freely varying.