NationMaster - Encyclopedia: Point estimation

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. Jump to: navigation, search Statistics is a type of data analysis which includes the planning, summarizing, and interpreting of observations of a system possibly followed by predicting or forecasting of future events based on a mathematical model of the system being observed. ... A sample is that part of a population which is actually observed. ... Jump to: navigation, search DATA (Debt, AIDS, Trade, Africa) was established in 2002 by Bono (Paul Hewson) of the Rock band U2, and Bobby Shiver, along with activists from the Jubilee 2000 Drop the Debt Campaign, as an organisaton focused on Justice, not charity. ... A statistic (singular) is the result of applying a statistical algorithm to a set of data. ... A parameter is a measurement or value on which something else depends. ...

More formally, it is the application of a point estimator to the data. In statistics, an estimator is a function of the known data that is used to estimate an unknown parameter; an estimate is the result from the actual application of the function to a particular set of 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). Jump to: navigation, search Bayesian inference is a statistical inference in which probabilities are interpreted not as frequencies or proportions or the like, but rather as degrees of belief. ... In Bayesian probability theory, the posterior probability is the conditional probability of some event or proposition, taking empirical data into account. ... In mathematics, a probability density function (pdf) serves to represent a probability distribution in terms of integrals. ...

See also

Categories: Statistics stubs | Statistics Jump to: navigation, search Maximum likelihood estimation (MLE) is a popular statistical method used to make inferences about parameters of the underlying probability distribution of a given data set. ... In statistics, the method of moments is a method of estimation of population parameters such as mean, variance, median, etc. ... The generalized method of moments is a very general statistical method for obtaining estimates of parameters of statistical models. ... In statistics, the CramÃ©r-Rao inequality, named in honor of Harald CramÃ©r and Calyampudi Radhakrishna Rao, states that the reciprocal of the Fisher information, , of a parameter , is a lower bound on the variance of an unbiased estimator of the parameter (denoted ). In some cases, no unbiased estimator... Minimum mean-square error (MMSE) relates to an estimator having estimates with the minimum mean squared error possible. ... In statistics, the method of maximum a posteriori (MAP, or posterior mode) estimation can be used to obtain a point estimate of an unobserved quantity on the basis of empirical data. ... In statistics, and more specifically in estimation theory, a minimum-variance unbiased estimator (MVUE or MVU estimator) is an unbiased estimator of parameters, whose variance is minimized for all values of the parameters. ... This article is not about Gauss-Markov processes. ... In statistics, a biased estimator is one that for some reason on average over- or underestimates what is being estimated. ... Result of particle filtering (red line) based on observed data generated from the blue line ( Much larger image) Particle filter methods, also known as Sequential Monte Carlo (SMC), are sophisticated model estimation techniques based on simulation. ... Markov chain Monte Carlo (MCMC) methods, sometimes called random walk Monte Carlo methods, are a class of algorithms for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its stationary distribution. ... Jump to: navigation, search The Kalman filter is an efficient recursive filter which estimates the state of a dynamic system from a series of incomplete and noisy measurements. ... The Wiener filter is a filter proposed by Norbert Wiener during the 1940s and published [1]. // Description Unlike the typical filtering theory of designing a filter for a desired frequency response the Wiener filter approaches filtering from a different angle. ...

Encyclopedia > Point estimation

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