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Principal component regression
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#	Item
211	SCGLR - An R Package for Supervised Component Generalized Linear Regression Fr´ed´eric Mortier∗, Catherine Trottier†‡, Guillaume Cornu∗and Xavier Bry‡ July 17, 2014 Summary: Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-07-17 10:58:19 Linear regression Generalized linear model Principal component analysis Least squares Partial least squares regression Degrees of freedom Dummy variable Instrumental variable Simultaneous equations model Statistics Regression analysis Econometrics
212	OMBRO—OMHCHO—OMOCLO De-Striping README FILE Date of this Document: 1 February 2007 Overview This document describes algorithm implementations of destriping corrections for the L2 products of the SAO PGEs OMBRO, OMHCH Add to Reading List Source URL: ozoneaq.gsfc.nasa.gov Language: English - Date: 2007-02-27 16:38:59 Error Measurement Robust statistics Outlier Errors and residuals in statistics Smoothing Principal component analysis Sunlight Statistics Data analysis Regression analysis
213	A General Regression Technique for Learning Transductions Corinna Cortes Google Research 1440 Broadway, New York, NY 10018, USA Mehryar Mohri Add to Reading List Source URL: www.machinelearning.org Language: English - Date: 2008-12-01 11:14:22 Natural language processing Signal processing Learning Support vector machine Kernel principal component analysis Regression analysis Perceptron String kernel N-gram Statistics Machine learning Statistical classification
214	RainMon: An Integrated Approach to Mining Bursty Timeseries Monitoring Data Ilari Shafer, Kai Ren, Vishnu Naresh Boddeti, Yoshihisa Abe, Gregory R. Ganger, Christos Faloutsos Metrics like disk activity and network traff Add to Reading List Source URL: www.pdl.cmu.edu Language: English - Date: 2013-02-12 17:28:59 Anomaly detection Time series Principal component analysis Kalman filter Regression analysis Statistics Data analysis Data mining
215	The University of British Columbia Add to Reading List Source URL: web.forestry.ubc.ca Language: English - Date: 2007-12-11 11:27:41 Multivariate analysis Factor analysis Linear regression Regression analysis Eigenvalues and eigenvectors Canonical correlation Chemometrics Statistics Multivariate statistics Principal component analysis
216	CARE: Finding Local Linear Correlations in High Dimensional Data Xiang Zhang, Feng Pan, and Wei Wang Department of Computer Science University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA Add to Reading List Source URL: www.cs.ucla.edu Language: English - Date: 2008-02-10 23:19:52 Singular value decomposition Data analysis Model selection Principal component analysis Feature selection Eigenvalues and eigenvectors Correlation and dependence Regression analysis Linear discriminant analysis Statistics Multivariate statistics Machine learning
217	Estimating GVAR weight matrices Add to Reading List Source URL: www.ecb.europa.eu Language: English - Date: 2013-03-06 05:17:12 Multivariate statistics Data analysis Statistical methods Vector autoregression Least squares Economic model Principal component analysis Variance Matrix Statistics Econometrics Regression analysis
218	Sparse logistic principal components analysis for binary data Add to Reading List Source URL: www.stat.tamu.edu Language: English - Date: 2010-03-16 21:45:21 Logistic regression Sparse PCA Normal distribution Multivariate normal distribution Statistics Multivariate statistics Principal component analysis
219	Reduced Rank Mixed Effects Models for Spatially Correlated Hierarchical Functional Data Lan Zhou, Jianhua Z. Huang, Josue G. Martinez, Arnab Maity, Veerabhadran Baladandayuthapani and Raymond J. Carroll Add to Reading List Source URL: www.stat.tamu.edu Language: English - Date: 2009-11-11 15:44:02 Econometrics Time series analysis Multivariate statistics Principal component analysis Multivariate normal distribution Covariance Regression analysis Variance Correlation and dependence Statistics Covariance and correlation Data analysis
220	Effective Linear Discriminant Analysis for High Dimensional, Low Sample Size Data Zhihua Qiao,∗ Lan Zhou† and Jianhua Z. Huang‡ Abstract— In the so-called high dimensional, low sample size (HDLSS) settings, LDA p Add to Reading List Source URL: www.stat.tamu.edu Language: English - Date: 2008-02-29 23:01:57 Data analysis Singular value decomposition Linear discriminant analysis Principal component analysis Eigenvalues and eigenvectors Covariance matrix Support vector machine Linear regression Variance Statistics Multivariate statistics Statistical classification

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