An Alternative to the MVU Estimator to Estimate the Level of DC in AWGN

Abstract

In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a particular statistical model, finding parameter values that maximize probability, observations, and the parameters are specified. The MLE can be seen as a special case of maximum post-positive estimation (MAP), which includes a uniform preventive distribution of parameters, or as a variant of the MAP that ignores the above and is therefore unregulated. Now let's look at an alternative to the MVU estimator, which is desirable in situations where the minimum variance unbiased (MVU) estimator does not exist or cannot be found, even if it exists. This estimator, which relies on the principle of maximum likelihood, is primarily the common method for obtaining a practical estimator. It has the clear advantage of being a crank turning procedure, which allows you to implement it for complicated estimation problems. A clear advantage of MLE is that it can be found numerically for a given data-set. The safest way to find the MLE is to search the grid, as long as the space between the searches are small enough, we are sure to find the MLE.

Keywords: Maximum Likelihood Estimation, minimum variance unbiased, Estimator, Probability Distribution Function.

DOI: 10.7176/ISDE/11-3-05

Publication date: June 30^th 2020