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  Browse All Reviews > Mathematics Of Computing (G) > Probability And Statistics (G.3) > Correlation And Regression Analysis (G.3...)  
 
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  1-10 of 10 Reviews about "Correlation And Regression Analysis (G.3...)": Date Reviewed
  A tutorial on canonical correlation methods
Uurtio V., Monteiro J., Kandola J., Shawe-Taylor J., Fernandez-Reyes D., Rousu J. ACM Computing Surveys 50(6): 1-33, 2018.  Type: Article

Canonical correlation analysis (CCA) is used to discover relations between two or more multivariate sets of variables, called views. Data to be processed are collected for a population of individuals, and for one individual its state i...

Apr 26 2018
  Using regression makes extraction of shared variation in multiple datasets easy
Korpela J., Henelius A., Ahonen L., Klami A., Puolamäki K. Data Mining and Knowledge Discovery 30(5): 1112-1133, 2016.  Type: Article

This interesting paper presents an application that could be of value to individuals working in data analysis of sets, trying to find commonalities among what appears to be unrelated data. The idea behind the derivation of shared varia...

Nov 23 2016
  Modern regression methods
Ryan T., Wiley-Interscience, New York, NY, 2008. 642 pp.  Type: Book (9780470081860)

One of the main objectives of science is to predict a future value y of a physical quantity. For this prediction, we must know how y depends on the current and past values, x, of...

Jul 13 2009
  Using multiple linear regression to forecast the number of asthmatics
Gabda D., Abdullah N., Budin K., Lim C.  Computer engineering and applications (Proceedings of the 2nd WSEAS International Conference on Computer Engineering and Applications, Acapulco, Mexico, Jan 25-27, 2008) 256-260, 2008.  Type: Proceedings

It is important to develop models that will predict the impact of environmental factors on the incidence of disease. In this paper, Gabda et al. formulate and test a number of linear-regression models that relate atmospheric metrics to...

Jul 8 2008
  Sample-based estimation of correlation ratio with polynomial approximation
Lewandowski D., Cooke R., Tebbens R. ACM Transactions on Modeling and Computer Simulation 18(1): 1-17, 2007.  Type: Article

Although relatively formulaic, this paper is flawless in its simplicity and elegance. After the obligatory introduction examining the importance of sensitivity measures and past techniques for evaluating them, the authors discuss globa...

Feb 21 2008
  Canonical correlation analysis of risk factors and clinical outcomes in cardiac surgery
Ridderstolpe L., Gill H., Borga M., Rutberg H., Åhlfeldt H. Journal of Medical Systems 29(4): 357-377, 2005.  Type: Article

Canonical correlation analysis (CCA) is interesting and important, since it provides an overall picture of associations between risk factors and outcome variables. Ridderstolpe et al. present a study that uses CCA to examine the relati...

Aug 14 2006
  Selecting the right objective measure for association analysis
Tan P., Kumar V. (ed), Srivastava J. Information Systems 29(4): 293-313, 2004.  Type: Article

The quality of an association pattern can be evaluated using many different measures, such as confidence, support, and interest. This makes it difficult to select the appropriate measure for a particular application. In this paper, the...

Mar 9 2005
  Using GMDH for modeling economical indices of mine opening
Sarycheva L. Systems Analysis Modelling Simulation 43(10): 1341-1349, 2003.  Type: Article

A static object is considered with m entries and one output. Object examination results are represented as a matrix and a vector. The problem of structural identification is solved. Model enumeration is realized, wit...

Mar 2 2004
  Statistical inference
Rohatgi V., Dover Publications, Incorporated, 2003. 948 pp.  Type: Book (9780486428123)

This excellent text examines and analyzes the relationship between probability and statistics. The concept of statistical inference is introduced at an early stage, and is mixed with a treatment of probability at all levels....

Jan 23 2004
  Strong convergence of estimators in nonlinear autoregressive models
Liebscher E. Journal of Multivariate Analysis 84(2): 247-261, 2003.  Type: Article

No structural surprises are presented in this paper, which contains main results, a relative application, and proofs. In general, however, the author lets his work do the talking. The author proves rates of strong convergence of
Aug 27 2003
 
 
 
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