Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. An estimator attempts to approximate the unknown parameters using the measurements. In estimation theory, two approaches are generally considered:
The probabilistic approach (described in this article) assumes that the measured data is random with probability distribution dependent on the parameters of interest The set-membership approach assumes that the measured data vector belongs to a set which depends on the parameter vector.
Examples
For example, it is desired to estimate the proportion of a population of voters who will vote for a particular candidate. That proportion is the parameter sought; the estimate is based on a small random sample of voters. Alternatively, it is desired to estimate the probability of a voter voting for a particular candidate, based on some demographic features, such as age.
Or, for example, in radar the aim is to find the range of objects (airplanes, boats, etc.) by analyzing the two-way transit timing of received echoes of transmitted pulses. Since the reflected pulses are unavoidably embedded in electrical noise, their measured values are randomly distributed, so that the transit time must be estimated.
As another example, in electrical communication theory, the measurements which contain information regarding the parameters of interest are often associated with a noisy signal.
Basics
For a given model, several statistical "ingredients" are needed so the estimator can be implemented. The first is a statistical sample – a set of data points taken from a random vector (RV) of size N. Put into a vector,
Secondly, there are M parameters
whose values are to be estimated. Third, the continuous probability density function (pdf) or its discrete counterpart, the probability mass function (pmf), of the underlying distribution that generated the data must be stated conditional on the values of the parameters:
It is also possible for the parameters themselves to have a probability distribution (e.g., Bayesian statistics). It is then necessary to define the Bayesian probability
After the model is formed, the goal is to estimate the parameters, with the estimates commonly denoted , where the "hat" indicates the estimate.
One common estimator is the minimum mean squared error (MMSE) estimator, which utilizes the error between the estimated parameters and the actual value of the parameters
as the basis for optimality. This error term is then squared and the expected value of this squared value is minimized for the MMSE estimator.
Estimators
Commonly used estimators (estimation methods) and topics related to them include: Maximum likelihood estimators Bayes estimators Method of moments estimators Cramér–Rao bound Least squares Minimum mean squared error (MMSE), also known as Bayes least squared error (BLSE) Maximum a posteriori (MAP) Minimum variance unbiased estimator (MVUE) Nonlinear system identification Best linear unbiased estimator (BLUE) Unbiased estimators — see estimator bias. Particle filter Markov chain Monte Carlo (MCMC) Kalman filter, and its various derivatives Wiener filter
Examples
Unknown constant in additive white Gaussian noise
Consider a received discrete signal, , of independent samples that consists of an unknown constant with additive white Gaussian noise (AWGN) with zero mean and known variance (i.e., ). Since the variance is known then the only unknown parameter is .
The model for the signal is then
Two possible (of many) estimators for the parameter are:
which is the sample mean
Both of these estimators have a mean of , which can be shown through taking the expected value of each estimator
and
At this point, these two estimators would appear to perform the same. However, the difference between them becomes apparent when comparing the variances.
and
It would seem that the sample mean is a better estimator since its variance is lower for every N > 1.
Maximum likelihood
Continuing the example using the maximum likelihood estimator, the probability density function (pdf) of the noise for one sample is
and the probability of becomes ( can be thought of a )