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Regression validation
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21	Grey-box modeling of the heat dynamics of a building with CTSM-R Rune Juhl Niels Rode Kristensen Peder Bacher Add to Reading List Source URL: ctsm.info Language: English - Date: 2013-04-03 03:58:43 Errors and residuals in statistics Autocorrelation R Statistics Regression analysis Regression model validation
22	Model Validation Kit – status and outlook H.R. Olesen National Environmental Research Institute (NERI) P.O. Box 358, DK-4000 Roskilde, Denmark E-mail Add to Reading List Source URL: www2.dmu.dk Language: English - Date: 1999-02-11 07:12:00 Atmosphere Air pollution Economic model Atmospheric dispersion modeling NAME Regression model validation Scientific modelling Macroeconomic model Scale Air dispersion modeling Statistics Science
23	Microsoft PowerPoint - SteveZech [Compatibility Mode] Add to Reading List Source URL: www.csss.washington.edu Language: English - Date: 2009-07-06 17:47:33 Regression analysis Model selection Maximum likelihood Statistical theory Cross-validation Statistical power Logistic regression Statistics Estimation theory M-estimators
24	Simple example of grey-box modeling of the heat dynamics of a wall with CTSM-R Rune Juhl Niels Rode Kristensen Peder Bacher Add to Reading List Source URL: ctsm.info Language: English - Date: 2013-09-07 18:13:55 Statistical models Regression model validation Statistical inference Least squares Statistics Regression analysis Regression diagnostics
25	Homework Assignment 1 The purpose of this homework is to apply linear regression on a real data set. We will consider three ways of improving prediction accuracy: feature selection, ridge regression and the lasso. We wil Add to Reading List Source URL: www.timvanerven.nl Language: English - Date: 2014-11-14 05:53:12 Model selection Econometrics Data analysis Cross-validation Training set Linear regression Least squares Lasso Test set Statistics Machine learning Regression analysis
26	The Estimation of Prediction Error: Covariance Penalties and Cross-Validation Bradley Efron ABSTRACT Having constructed a data-based estimation rule, perhaps a logistic regression or a classification tree, the statistici Add to Reading List Source URL: statweb.stanford.edu Language: English - Date: 2009-10-02 15:59:10
27	ASSIGNMENT 12. VALIDATING THE REGRESSION MODEL 1. Run geosa11.m, selecting part 2 (assignment 12) from the opening menu. This selection specifies multiple linear regression with cross-validation. The difference from assi Add to Reading List Source URL: www.ltrr.arizona.edu Language: English - Date: 2015-04-10 12:59:37
28	Testing fit patterns with polynomial regression models Felix Schönbrodt Ludwig-Maximilians-Universität München, Germany RA FT Add to Reading List Source URL: www.psy.lmu.de Language: English - Date: 2015-03-31 02:51:02 Akaike information criterion Polynomial regression Coefficient of determination Degrees of freedom Curve fitting Cross-validation Model selection Nonparametric regression Statistics Regression analysis Linear regression
29	An ensemble of classiﬁers approach with multiple sources of information Roberto Santana, Concha Bielza, Pedro Larrañaga Departamento de Inteligencia Artiﬁcial, Universidad Politécnica de Madrid roberto.santana@upm. Add to Reading List Source URL: www.cis.hut.fi Language: English - Date: 2012-01-05 06:58:35 Machine learning Econometrics Covariance and correlation Correlation and dependence Feature selection Decision tree learning Cross-validation Logistic regression Statistics Regression analysis Model selection
30	B ARCELONA G RADUATE S CHOOL OF E CONOMICS - D ATA S CIENCE Random Forest Cover Type Prediction Jéssica Leal, Tim Kreienkamp and Philipp Schmidt Add to Reading List Source URL: dl.dropboxusercontent.com Language: English Ensemble learning Decision trees Regression analysis Random forest Cross-validation Overfitting Gradient boosting Boosting Decision tree learning Machine learning Statistics Computational statistics

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