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Nonlinear Regression Analysis of the Joint-Regression Model
Biometrics, 1997Summary: The joint-regression model for two-way data assumes a linear relation between a continuous response and column effects. Standard methods for fitting the model condition on estimates of the column effects, but including column effects as covariates in the model results in a nonlinear estimation problem.
Ng, Meei Pyng, Grunwald, Gary K.
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Clustering by Regression Analysis
2003In data clustering, many approaches have been proposed such as K-means method and hierarchical method. One of the problems is that the results depend heavily on initial values and criterion to combine clusters.
Masahiro Motoyoshi +2 more
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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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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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2015
This chapter discusses regression analysis. It begins with the familiar process of drawing a best-fit straight line through a set of experimental data points on an x–y graph, and then derives the statistics in a format that links into the more advanced analyses developed throughout the rest of the book.
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This chapter discusses regression analysis. It begins with the familiar process of drawing a best-fit straight line through a set of experimental data points on an x–y graph, and then derives the statistics in a format that links into the more advanced analyses developed throughout the rest of the book.
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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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