Results 261 to 270 of about 26,999,079 (309)
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CVPR 2011, 2011
A new paradigm for multivariate regression is proposed; principal regression analysis (PRA). It entails learning a low dimensional subspace over sample-specific regressors. For a given input, the model predicts a subspace thought to contain the corresponding response. Using this subspace as a prior, the search space is considerably more constrained. An
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A new paradigm for multivariate regression is proposed; principal regression analysis (PRA). It entails learning a low dimensional subspace over sample-specific regressors. For a given input, the model predicts a subspace thought to contain the corresponding response. Using this subspace as a prior, the search space is considerably more constrained. An
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2003
Abstract As discussed in chapter 6, the nonlinear nature of structured finance significantly complicates the credit analysis of structured securities and requires the use of iterative solution procedures that fully articulate the codependence between the shape of the yield curve and the target credit ratings.
Sylvain Raynes, Ann Rutledge
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Abstract As discussed in chapter 6, the nonlinear nature of structured finance significantly complicates the credit analysis of structured securities and requires the use of iterative solution procedures that fully articulate the codependence between the shape of the yield curve and the target credit ratings.
Sylvain Raynes, Ann Rutledge
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Regression and Correlation Analysis
1987Correlation is a tool for understanding the relationship between two quantities. Regression considers how one quantity is influenced by another. In correlation analysis the two quantities are considered symmetrically: in regression analysis one is supposed dependent on the other, in an unsymmetric way. Extensions to sets of quantities are important.
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Correlation and regression analysis
Annals of Allergy, Asthma & Immunology, 2009Runhua, Shi, Steven A, Conrad
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Regression Analysis by Example.
Journal of the American Statistical Association, 1979G. K. Kanji, S. Chatterjee, B. Price
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Joint Regression Analysis and Completed Joint Regression Analysis
2012Joint Regression Analysis (JRA) is a well known technique for the joint analysis of series of cultivar comparison trials. Formerly these trials were usually designed as complete randomized block designs. Now, the -designs are the mostly used. To perform a JRA, a linear regression is adjusted for each cultivar.
Pereira, Dulce G. +3 more
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