Branch of statistics to estimate models based on measured data
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.
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 noisysignal.
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:
and finding the negative expected value is trivial since it is now a deterministic constant
Finally, putting the Fisher information into
Comparing this to the variance of the sample mean (determined previously) shows that the sample mean is equal to the Cramér–Rao lower bound for all values of and .
In other words, the sample mean is the (necessarily unique) efficient estimator, and thus also the minimum variance unbiased estimator (MVUE), in addition to being the maximum likelihood estimator.
Maximum of a uniform distribution
One of the simplest non-trivial examples of estimation is the estimation of the maximum of a uniform distribution. It is used as a hands-on classroom exercise and to illustrate basic principles of estimation theory. Further, in the case of estimation based on a single sample, it demonstrates philosophical issues and possible misunderstandings in the use of maximum likelihood estimators and likelihood functions.
Babak Hassibi, Ali H. Sayed, and Thomas Kailath, Indefinite Quadratic Estimation and Control: A Unified Approach to H2 and H Theories, Society for Industrial & Applied Mathematics (SIAM), PA, 1999, ISBN 978-0-89871-411-1.
V.G.Voinov, M.S.Nikulin, "Unbiased estimators and their applications. Vol.1: Univariate case", Kluwer Academic Publishers, 1993, ISBN 0-7923-2382-3.
V.G.Voinov, M.S.Nikulin, "Unbiased estimators and their applications. Vol.2: Multivariate case", Kluwer Academic Publishers, 1996, ISBN 0-7923-3939-8.
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