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Statements

Subject Item: dbr:Regression_validation
dbo:wikiPageWikiLink: dbr:Mean_squared_prediction_error

Subject Item: dbr:Mean_squared_prediction_error
rdf:type: yago:Abstraction100002137 yago:Function113783816 yago:WikicatLossFunctions yago:MathematicalRelation113783581 yago:Relation100031921
rdfs:label: Mean squared prediction error
rdfs:comment: In statistics the mean squared prediction error or mean squared error of the predictions of a smoothing or curve fitting procedure is the expected value of the squared difference between the fitted values implied by the predictive function and the values of the (unobservable) function 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. If the smoothing or fitting procedure has projection matrix (i.e., hat matrix) L, which maps the observed values vector to predicted values vector via then
dcterms:subject: dbc:Statistical_deviation_and_dispersion dbc:Loss_functions dbc:Point_estimation_performance
dbo:wikiPageID: 3244288
dbo:wikiPageRevisionID: 1105990111
dbo:wikiPageWikiLink: dbr:Colin_Mallows dbr:Mallows's_Cp dbr:Data_analyst dbr:Curve_fitting dbr:Errors_and_residuals_in_statistics dbr:Statistical_population dbc:Statistical_deviation_and_dispersion dbc:Loss_functions dbr:Projection_matrix dbr:Law_of_total_variance dbr:Regression_analysis dbr:Mean_squared_error dbr:Cross-validation_(statistics) dbc:Point_estimation_performance dbr:Sample_(statistics) dbr:Statistics dbr:Smoothing
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dbo:abstract: In statistics the mean squared prediction error or mean squared error of the predictions of a smoothing or curve fitting procedure is the expected value of the squared difference between the fitted values implied by the predictive function and the values of the (unobservable) function 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. If the smoothing or fitting procedure has projection matrix (i.e., hat matrix) L, which maps the observed values vector to predicted values vector via then The MSPE can be decomposed into two terms: the mean of squared biases of the fitted values and the mean of variances of the fitted values: Knowledge of g is required in order to calculate the MSPE exactly; otherwise, it can be estimated.
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foaf:isPrimaryTopicOf: wikipedia-en:Mean_squared_prediction_error

Subject Item: dbr:Mean_percentage_error
dbo:wikiPageWikiLink: dbr:Mean_squared_prediction_error

Subject Item: dbr:Forecasting
dbo:wikiPageWikiLink: dbr:Mean_squared_prediction_error

Subject Item: dbr:Forecast_error
dbo:wikiPageWikiLink: dbr:Mean_squared_prediction_error

Subject Item: dbr:Autoregressive_model
dbo:wikiPageWikiLink: dbr:Mean_squared_prediction_error

Subject Item: dbr:MSPE
dbo:wikiPageWikiLink: dbr:Mean_squared_prediction_error
dbo:wikiPageDisambiguates: dbr:Mean_squared_prediction_error

Subject Item: dbr:Mean_squared_error
dbo:wikiPageWikiLink: dbr:Mean_squared_prediction_error

Subject Item: dbr:List_of_statistics_articles
dbo:wikiPageWikiLink: dbr:Mean_squared_prediction_error

Subject Item: dbr:Outline_of_machine_learning
dbo:wikiPageWikiLink: dbr:Mean_squared_prediction_error

Subject Item: dbr:Prediction_error
dbo:wikiPageWikiLink: dbr:Mean_squared_prediction_error
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Subject Item: wikipedia-en:Mean_squared_prediction_error
foaf:primaryTopic: dbr:Mean_squared_prediction_error