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Two nonparametric methods for multiple regression transform selection are provided. The first, Alternative Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R.. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction to these two methods is in chapter 16 of Frank Harrel's "Regression Modeling Strategies" in the Springer Series in Statistics.
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15 lines
1,005 B
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Two nonparametric methods for multiple regression transform selection
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are provided. The first, Alternative Conditional Expectations (ACE),
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is an algorithm to find the fixed point of maximal correlation, i.e.
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it finds a set of transformed response variables that maximizes R^2
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using smoothing functions [see Breiman, L., and J.H. Friedman. 1985.
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"Estimating Optimal Transformations for Multiple Regression and
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Correlation". Journal of the American Statistical Association.
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80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is
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the Additivity Variance Stabilization (AVAS) method which works better
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than ACE when correlation is low [see Tibshirani, R.. 1986.
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"Estimating Transformations for Regression via Additivity and Variance
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Stabilization". Journal of the American Statistical Association.
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83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction
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to these two methods is in chapter 16 of Frank Harrel's "Regression
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Modeling Strategies" in the Springer Series in Statistics.
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