Making and Evaluating Point Forecasts

Typically, point forecasting methods are compared and assessed by means of an error measure or scoring function, with the absolute error and the squared error being key examples. The individual scores are averaged over forecast cases, to result in a summary measure of the predictive performance, suc...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of the American Statistical Association Jg. 106; H. 494; S. 746 - 762
1. Verfasser:	Gneiting, Tilmann
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria, VA Taylor & Francis 01.06.2011 American Statistical Association Assoc Taylor & Francis Ltd
Schlagworte:	Applications Bayes rule Bayesian analysis Bregman function Conditional value-at-risk (CVaR) Decision theory Economic forecasting Elicitability Error Estimation Exact sciences and technology Expectation Expectations Expectile Financial theory Forecasting Forecasting techniques Function Functional analysis General topics Inference from stochastic processes; time series analysis Insurance, economics, finance Mathematical analysis Mathematical functions Mathematics Mean Mean square errors Measurement Median Methodology Mode Point forecasts Popularity Probabilities Probability and statistics Probability distributions Probability forecasts Proper scoring rule Quantile Review Article Rules Sciences and techniques of general use Scores Special functions Statistical forecasts Statistical functional Statistics Time series forecasting Weather forecasting Weighting Mean Conditional distribution Generalized linear model Error estimation Predictive distribution Prediction theory Median Statistical functional Stochastic process Bregman function Absolute error Forecasting theory Elicitability Quantile Bayes estimation Bayes rule Finance Predictive measure Conditional value-at-risk (CVaR) Mode Statistical estimation Proper scoring rule Statistical decision Mean estimation Functional Statistical method Statistical regression Expectile Decision theory Weight function Filtering theory Mean error
ISSN:	0162-1459, 1537-274X
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!