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Activity Number:
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77
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303444 |
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Title:
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Asymptotic Confidence Intervals in Ridge Regression
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Author(s):
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Luis Firinguetti*+ and Gladys Bobadilla
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Companies:
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Universidad del Bío Bío and Univesridad de Santiago
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Address:
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Avda. Collao 1202 - Casilla 5-C, Concepción, , Chile
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Keywords:
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Ridge Regression ; Multicollinearity ; Edgeworth expansion ; Asymptotic confidence intervals
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Abstract:
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Ridge Regression techniques have been found useful to reduce mean square errors of parameter estimates when multicollinearity is present. However, the usefulness of the method rests not only upon its ability to produce good parameter estimates, with smaller mean squared error than Ordinary Least Squares, but also on having reasonable inferential procedures. The aim of this paper is to develop asymptotic confidence intervals for the model parameters based on Ridge Regression estimates and the Edgeworth expansion. Some simulation experiments are carried out to compare these confidence intervals with those obtained from the application of Ordinary Least Squares. Also, an example will be provided based on the well known data set of Hald.
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