Point estimation
From Wikipedia, the free encyclopedia
In statistics, point estimation involves the use of sample data to calculate a single value (known as a statistic) which is to serve as a "best guess" for an unknown (fixed or random) population parameter.
More formally, it is the application of a point estimator to the data.
Point estimation should be contrasted with Bayesian methods of estimation, where the goal is usually to compute (perhaps to an approximation) the posterior distributions of parameters and other quantities of interest. The contrast here is between estimating a single point (point estimation), versus estimating a weighted set of points (a probability density function).
[edit] See also
- maximum likelihood (ML)
- method of moments, generalized method of moments
- Cramér-Rao inequality
- minimum mean squared error (MMSE)
- maximum a posteriori (MAP)
- minimum variance unbiased estimator (MVUE)
- best linear unbiased estimator (BLUE)
- Unbiased estimators — see bias (statistics).
- particle filter
- Markov chain Monte Carlo (MCMC)
- Kalman filter
- Wiener filter
