minimax estimator
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Minimax estimator — In statistical decision theory, where we are faced with the problem of estimating a deterministic parameter (vector) from observations an estimator (estimation rule) is called minimax if its maximal risk is minimal among all estimators of . In a… … Wikipedia
Minimax — This article is about the decision theory concept. For other uses, see Minimax (disambiguation). Minimax (sometimes minmax) is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss for a… … Wikipedia
James-Stein estimator — The James Stein estimator is a nonlinear estimator which can be shown to dominate, or outperform, the ordinary (least squares) technique. As such, it is the best known example of Stein s phenomenon.An earlier version of the estimator was… … Wikipedia
List of statistics topics — Please add any Wikipedia articles related to statistics that are not already on this list.The Related changes link in the margin of this page (below search) leads to a list of the most recent changes to the articles listed below. To see the most… … Wikipedia
List of mathematics articles (M) — NOTOC M M estimator M group M matrix M separation M set M. C. Escher s legacy M. Riesz extension theorem M/M/1 model Maass wave form Mac Lane s planarity criterion Macaulay brackets Macbeath surface MacCormack method Macdonald polynomial Machin… … Wikipedia
Regret (decision theory) — Regret (often also called opportunity loss) is defined as the difference between the actual payoff and the payoff that would have been obtained if a different course of action had been chosen. This is also called difference regret. Furthermore,… … Wikipedia
Robust statistics — provides an alternative approach to classical statistical methods. The motivation is to produce estimators that are not unduly affected by small departures from model assumptions. Contents 1 Introduction 2 Examples of robust and non robust… … Wikipedia
Linear least squares — is an important computational problem, that arises primarily in applications when it is desired to fit a linear mathematical model to measurements obtained from experiments. The goals of linear least squares are to extract predictions from the… … Wikipedia
Linear least squares/Proposed — Linear least squares is an important computational problem, that arises primarily in applications when it is desired to fit a linear mathematical model to observations obtained from experiments. Mathematically, it can be stated as the problem of… … Wikipedia
Loss function — In statistics and decision theory a loss function is a function that maps an event onto a real number intuitively representing some cost associated with the event. Typically it is used for parameter estimation, and the event in question is some… … Wikipedia
Chebyshev center — In geometry, the Chebyshev center of a bounded set Q having non empty interior is the center of the minimal radius ball enclosing the entire set Q. In the field of parameter estimation, the Chebyshev center approach tries to find an estimator for … Wikipedia
