About: Statistical model validation

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Statistical model validation Goto Sponge NotDistinct Permalink

An Entity of Type : owl:Thing, within Data Space : dbpedia.demo.openlinksw.com associated with source document(s)
QRcode icon

http://dbpedia.demo.openlinksw.com/describe/?url=http%3A%2F%2Fdbpedia.org%2Fresource%2FStatistical_model_validation

In statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model. To combat this, model validation is used to test whether a statistical model can hold up to permutations in the data. This topic is not to be confused with the closely related task of model selection, the process of discriminating between multiple candidate models: model validation does not concern so much the conceptual design of models as it tests only the consistency between a chosen model and its stated outputs.

Attributes	Values
rdfs:label	Statistical model validation (en) Затверджування статистичної моделі (uk)
rdfs:comment	У статистиці, затве́рджування моде́лі (англ. model validation) — це задача підтверджування того, що виходи статистичної моделі є прийнятними по відношенню до справжнього процесу, що породжує дані. Іншими словами, затверджування моделі є задачею підтверджування, що виходи статистичної моделі мають достатню точність відтворювання виходів процесу, що породжує дані, щоби було можливо досягати цілей дослідження. (uk) In statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model. To combat this, model validation is used to test whether a statistical model can hold up to permutations in the data. This topic is not to be confused with the closely related task of model selection, the process of discriminating between multiple candidate models: model validation does not concern so much the conceptual design of models as it tests only the consistency between a chosen model and its stated outputs. (en)
foaf:depiction
dcterms:subject	Validity (statistics) Statistical models
Wikipage page ID	26502065 (xsd:integer)
Wikipage revision ID	1117288884 (xsd:integer)
Link from a Wikipage to another Wikipage	Encyclopedia of Statistical Sciences Regression validation Validity (statistics) Internal validity John Wiley & Sons Predictive modelling Errors and residuals Model selection Validity (statistics) Cross-validation (statistics) Regression model validation Stack Exchange Statistical model Statistics Identifiability analysis Perplexity Spurious relationship Turing test All models are wrong Goodness of fit Statistical conclusion validity Pseudorandom number generator Regression analysis Residual sum of squares Statistical model specification Statistical models Springer Science+Business Media National Institute of Standards and Technology Sensitivity analysis External validity Stochastic simulation Overfitting Statistical model selection Model identification Predictive model
Link from a Wikipage to an external page	https://stats.stackexchange.com/q/291481 http://www.itl.nist.gov/div898/handbook/pmd/section4/pmd44.htm
sameAs	Statistical model validation Statistical model validation Statistical model validation
dbp:wikiPageUsesTemplate	dbt:Citation dbt:Cite_web dbt:Div_col dbt:Div_col_end dbt:Expand_section dbt:Redirect dbt:Reflist dbt:See
thumbnail	wiki-commons:Special:FilePath/Overfitted_Data.png?width=300
has abstract	In statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model. To combat this, model validation is used to test whether a statistical model can hold up to permutations in the data. This topic is not to be confused with the closely related task of model selection, the process of discriminating between multiple candidate models: model validation does not concern so much the conceptual design of models as it tests only the consistency between a chosen model and its stated outputs. There are many ways to validate a model. Residual plots plot the difference between the actual data and the model's predictions: correlations in the residual plots may indicate a flaw in the model. Cross validation is a method of model validation that iteratively refits the model, each time leaving out just a small sample and comparing whether the samples left out are predicted by the model: there are many kinds of cross validation. Predictive simulation is used to compare simulated data to actual data. External validation involves fitting the model to new data. Akaike information criterion estimates the quality of a model. (en) У статистиці, затве́рджування моде́лі (англ. model validation) — це задача підтверджування того, що виходи статистичної моделі є прийнятними по відношенню до справжнього процесу, що породжує дані. Іншими словами, затверджування моделі є задачею підтверджування, що виходи статистичної моделі мають достатню точність відтворювання виходів процесу, що породжує дані, щоби було можливо досягати цілей дослідження. (uk)
prov:wasDerivedFrom	wikipedia-en:Statistical_model_validation?oldid=1117288884&ns=0
page length (characters) of wiki page	10606 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Statistical_model_validation
is rdfs:seeAlso of	Statistical inference
is Link from a Wikipage to another Wikipage of	Prediction interval Bridget Rosewell Anscombe's quartet Regression validation Relative likelihood Residual analysis Validity (statistics) Internal validity Predictive power Box–Jenkins method Model selection Statistical model Perplexity Spurious relationship Data analysis Video quality Akaike information criterion Goodness of fit Knowledge distillation Statistical conclusion validity Statistical model specification Statistical assumption Validation Verification and validation List of statistics articles Model validation
is Wikipage redirect of	Residual analysis Model validation
is Wikipage disambiguates of	Validation
is foaf:primaryTopic of	wikipedia-en:Statistical_model_validation

Faceted Search & Find service v1.17_git139 as of Feb 29 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3330 as of Mar 19 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (378 GB total memory, 60 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software