Results 61 to 70 of about 284,636 (114)
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British Journal of Mathematical and Statistical Psychology, 1976
Regression component decompositions (RCD) are defined as a special class of component decompositions where the pattern contains the regression weights for predicting the observed variables from the latent variables. Compared to factor analysis, RCD has a broader range of applicability, greater ease and simplicity of computation, and a more logical and ...
Schönemann, Peter H., Steiger, James H.
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Regression component decompositions (RCD) are defined as a special class of component decompositions where the pattern contains the regression weights for predicting the observed variables from the latent variables. Compared to factor analysis, RCD has a broader range of applicability, greater ease and simplicity of computation, and a more logical and ...
Schönemann, Peter H., Steiger, James H.
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Regression Model for Better Generalization and Regression Analysis
Proceedings of the 4th International Conference on Machine Learning and Soft Computing, 2020Regression models such as polynomial regression when deployed for training on training instances may sometimes not optimize well and leads to poor generalization on new training instances due to high bias or underfitting due to small value of polynomial degree and may lead to high variance or overfitting due to high degree of polynomial fitting degree.
Mohiuddeen Khan, Kanishk Srivastava
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International Statistical Review / Revue Internationale de Statistique, 1992
Summary: The method of least squares ranks as one of the most commonly used methods for estimating the relation between a set of variables on the conditional expected value of another variable. Ordinary Least Squares (OLS) relies on several assumptions, which when violated may not yield robust estimates. We pose alternative ways to view this model.
Olkin, Ingram, Yitzhaki, Shlomo
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Summary: The method of least squares ranks as one of the most commonly used methods for estimating the relation between a set of variables on the conditional expected value of another variable. Ordinary Least Squares (OLS) relies on several assumptions, which when violated may not yield robust estimates. We pose alternative ways to view this model.
Olkin, Ingram, Yitzhaki, Shlomo
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Proceedings of the eighth international conference on APL - APL '76, 1976
The theory of multiple linear regression is developed using APL as a notation. The results of the analysis are then incorporated in a documented set of APL functions for performing regression calculations.
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The theory of multiple linear regression is developed using APL as a notation. The results of the analysis are then incorporated in a documented set of APL functions for performing regression calculations.
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Statistical Papers, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kosfeld, Reinhold +1 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kosfeld, Reinhold +1 more
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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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Regression-Discontinuity Analysis
2008The regression discontinuity (RD) data design is a quasi-experimental evaluation design first introduced by Thistlethwaite and Campbell (1960) as an alternative approach to evaluating social programmes. The design is characterized by a treatment assignment or selection rule which involves the use of a known cut-off point with respect to a continuous ...
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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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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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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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