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Arthur E. Hoerl
Researcher at University of Delaware
Publications - 12
Citations - 11731
Arthur E. Hoerl is an academic researcher from University of Delaware. The author has contributed to research in topics: Total least squares & Generalized least squares. The author has an hindex of 9, co-authored 12 publications receiving 10230 citations.
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Ridge regression: biased estimation for nonorthogonal problems
TL;DR: In this paper, an estimation procedure based on adding small positive quantities to the diagonal of X′X was proposed, which is a method for showing in two dimensions the effects of nonorthogonality.
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Ridge Regression: Applications to Nonorthogonal Problems
TL;DR: In this paper, the use of ridge regression methods is discussed and recommendations are made for obtaining a better regression equation than that given by ordinary least squares estimation. But the authors focus on the RIDGE TRACE which is a two-dimensional graphical procedure for portraying the complex relationships in multifactor data.
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Ridge regression:some simulations
TL;DR: In this paper, an algorithm is given for acting the biasing paramatar, k, in RIDGE regrassion, which has the following properties: (i) it produces an aberaged squared error for the regression coafficiants that is les than least squares, (ii) the distribuction of squared arrots for the regressors has a smallar variance than does that for last squares, and (iii) regradless of he signal-to-noiss retio the probability that RIDge produces a smaller squared error than least square is
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Ridge regression iterative estimation of the biasing parameter
TL;DR: In this paper, an iterative method is given for selecting the biasing parameter, k, in RIDGE regression, which produces a distribution of squared errors for the regression coefficients that has a smaller mean and a smaller variance than least squares or the single iteration estimate.
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A simulation of biased estimation and subset selection regression techniques
TL;DR: The authors compared three biased estimation and four subset selection regression techniques to least squares in a large-scale simulation and found that neither biased estimation nor subset selection demonstrated a consistent superiority over the other, excluding stepwise and principal component regression, both of which performed poorly.