derived estimator

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• Bayes estimator — In decision theory and estimation theory, a Bayes estimator is an estimator or decision rule that maximizes the posterior expected value of a utility function or minimizes the posterior expected value of a loss function (also called posterior… …   Wikipedia

• Invariant estimator — In statistics, the concept of being an invariant estimator is a criterion that can be used to compare the properties of different estimators for the same quantity. It is a way of formalising the idea that an estimator should have certain… …   Wikipedia

• Leonard-Merritt mass estimator — The Leonard Merritt mass estimator is a formula firstderived by Peter Leonard and David Merritt [Leonard, P. and Merritt, D. (1989). [http://adsabs.harvard.edu/abs/1989ApJ...339..195L| The mass of the open star cluster M35 as derived from proper… …   Wikipedia

• Basic pitch count estimator — In baseball statistics, the Basic Pitch Count Estimator is a statistic used to try to estimate the number of pitches thrown by a pitcher where there is no pitch count data available. The formula was first derived by Tom M. Tango. The formula is 3 …   Wikipedia

• Normal distribution — This article is about the univariate normal distribution. For normally distributed vectors, see Multivariate normal distribution. Probability density function The red line is the standard normal distribution Cumulative distribution function …   Wikipedia

• Kalman filter — Roles of the variables in the Kalman filter. (Larger image here) In statistics, the Kalman filter is a mathematical method named after Rudolf E. Kálmán. Its purpose is to use measurements observed over time, containing noise (random variations)… …   Wikipedia

• Ordinary least squares — This article is about the statistical properties of unweighted linear regression analysis. For more general regression analysis, see regression analysis. For linear regression on a single variable, see simple linear regression. For the… …   Wikipedia

• Least squares — The method of least squares is a standard approach to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns. Least squares means that the overall solution minimizes the sum of… …   Wikipedia

• Sample maximum and minimum — Box plots of the Michelson–Morley experiment, showing sample maximums and minimums. In statistics, the maximum and sample minimum, also called the largest observation, and smallest observation, are the values of the greatest and least elements of …   Wikipedia

• Analysis of variance — In statistics, analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of… …   Wikipedia

• Maximum spacing estimation — The maximum spacing method tries to find a distribution function such that the spacings, D(i), are all approximately of the same length. This is done by maximizing their geometric mean. In statistics, maximum spacing estimation (MSE or MSP), or… …   Wikipedia