Results 251 to 260 of about 177,424 (286)
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1980
Abstract : This work examined minimax linear estimation in multiple linear regression. The application of minimax estimation to regression led to the development of ridge regression estimators with stochastic ridge parameters. These estimators were seen to be invariant under linear transformation; a property which has not been established for other ...
Thomas P. Ryan, Lawrence C. Peele
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Abstract : This work examined minimax linear estimation in multiple linear regression. The application of minimax estimation to regression led to the development of ridge regression estimators with stochastic ridge parameters. These estimators were seen to be invariant under linear transformation; a property which has not been established for other ...
Thomas P. Ryan, Lawrence C. Peele
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The American Statistician, 1975
Summary The use of biased estimation in data analysis and model building is discussed. A review of the theory of ridge regression and its relation to generalized inverse regression is presented along with the results of a simulation experiment and three examples of the use of ridge regression in practice.
Donald W. Marquardt, Ronald D. Snee
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Summary The use of biased estimation in data analysis and model building is discussed. A review of the theory of ridge regression and its relation to generalized inverse regression is presented along with the results of a simulation experiment and three examples of the use of ridge regression in practice.
Donald W. Marquardt, Ronald D. Snee
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The American Statistician, 1987
Abstract A useful matrix result is applied in ridge regression to cast light on the form of the ridge-regression residual sum of squares as a power series in the ridge parameter. An example illustrates that the series may diverge or else converge very slowly, depending on the value of the ridge parameter.
Norman R. Draper, Agnes M. Herzberg
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Abstract A useful matrix result is applied in ridge regression to cast light on the form of the ridge-regression residual sum of squares as a power series in the ridge parameter. An example illustrates that the series may diverge or else converge very slowly, depending on the value of the ridge parameter.
Norman R. Draper, Agnes M. Herzberg
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Heteroscedastic kernel ridge regression
Neurocomputing, 2004In this paper we extend a form of kernel ridge regression (KRR) for data characterised by a heteroscedastic (i.e. input dependent variance) Gaussian noise process, introduced in Foxall et al. (in: Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002), Bruges, Belgium, April 2002, pp. 19–24).
Cawley, Gavin C. +4 more
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On multivariate ridge regression
1985A multivariate linear regression model with q responses as a linear function ofpindependent variables ry,= + k is considered withapxqparameter matrix B. The least squares (or Maximum Likelihood for multivariate normal E) % % estimator of B is deficient in that it takes no account of the % "across regression" correlations, on the one hand, and ignores ...
Haitovsky, Y, Haitovsky, Y
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2013
This chapter discusses the method of Kernel Ridge Regression, which is a very simple special case of Support Vector Regression. The main formula of the method is identical to a formula in Bayesian statistics, but Kernel Ridge Regression has performance guarantees that have nothing to do with Bayesian assumptions.
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This chapter discusses the method of Kernel Ridge Regression, which is a very simple special case of Support Vector Regression. The main formula of the method is identical to a formula in Bayesian statistics, but Kernel Ridge Regression has performance guarantees that have nothing to do with Bayesian assumptions.
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Length modified ridge regression
Computational Statistics & Data Analysis, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A thin mantle transition zone beneath the equatorial Mid-Atlantic Ridge
Nature, 2021Matthew R Agius +2 more
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