Publication

Conditionally optimal weights of evidence

Stephan Morgenthaler, Robert Staudte
2005
Journal Articles

Abstract

A weight of evidence is a calibrated statistic whose values in [0, 1] indicate the degree of agreement between the data and either of two hypothesis, one being treated as the null (H 0) and the other as the alternative (H 1). A value of zero means perfect agreement with the null, whereas a value of one means perfect agreement with the alternative. The optimality we consider is minimal mean squared error (MSE) under the alternative while keeping the MSE under the null below a fixed bound. This paper studies such statistics from a conditional point of view, in particular for location and scale models. © Springer-Verlag 2005.

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Stephan Morgenthaler, Robert Staudte
2005
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/2cd0ffc3-c185-4987-a303-7840c3f3f24e

About this result

Related concepts (32)

Mean squared error

In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate.

Academic degree

An academic degree is a qualification awarded to a student upon successful completion of a course of study in higher education, usually at a college or university. These institutions often offer degrees at various levels, usually divided into undergraduate and postgraduate degrees. The most common undergraduate degree is the bachelor's degree, although some educational systems offer lower level undergraduate degrees such as associate and foundation degrees. Common postgraduate degrees include master's degrees and doctorates.

Mean squared prediction error

In statistics the mean squared prediction error (MSPE), also known as mean squared error of the predictions, of a smoothing, curve fitting, or regression procedure is the expected value of the squared prediction errors (PE), the square difference between the fitted values implied by the predictive function and the values of the (unobservable) true value g. It is an inverse measure of the explanatory power of and can be used in the process of cross-validation of an estimated model.

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