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Spline Regression in the Presence of Categorical Predictors.

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Τίτλος: Spline Regression in the Presence of Categorical Predictors.
Συγγραφείς: Ma, Shujie1,2, Racine, Jeffrey S.3,4, Yang, Lijian2
Πηγή: Journal of Applied Econometrics. Aug2015, Vol. 30 Issue 5, p705-717. 13p.
Θεματικοί όροι: *Regression analysis, *Monte Carlo method, *Mathematical models, Regression analysis data processing, Numerical analysis
Περίληψη: We consider the problem of estimating a relationship nonparametrically using regression splines when there exist both continuous and categorical predictors. We combine the global properties of regression splines with the local properties of categorical kernel functions to handle the presence of categorical predictors rather than resorting to sample splitting as is typically done to accommodate their presence. The resulting estimator possesses substantially better finite-sample performance than either its frequency-based peer or cross-validated local linear kernel regression or even additive regression splines (when additivity does not hold). Theoretical underpinnings are provided and Monte Carlo simulations are undertaken to assess finite-sample behavior; and two illustrative applications are provided. An implementation in R is available; see the R package 'crs' for details. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Econometrics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: We consider the problem of estimating a relationship nonparametrically using regression splines when there exist both continuous and categorical predictors. We combine the global properties of regression splines with the local properties of categorical kernel functions to handle the presence of categorical predictors rather than resorting to sample splitting as is typically done to accommodate their presence. The resulting estimator possesses substantially better finite-sample performance than either its frequency-based peer or cross-validated local linear kernel regression or even additive regression splines (when additivity does not hold). Theoretical underpinnings are provided and Monte Carlo simulations are undertaken to assess finite-sample behavior; and two illustrative applications are provided. An implementation in R is available; see the R package 'crs' for details. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Applied Econometrics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/jae.2410
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 705
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      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Regression analysis data processing
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      – SubjectFull: Numerical analysis
        Type: general
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      – TitleFull: Spline Regression in the Presence of Categorical Predictors.
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            NameFull: Ma, Shujie
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            NameFull: Racine, Jeffrey S.
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            – D: 01
              M: 08
              Text: Aug2015
              Type: published
              Y: 2015
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