Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Adaptive Regression by Mixing for Fixed Design

  • Oh, Jong-Chul (Department of Informatics and Statistics, Kunsan National University) ;
  • Lu, Yun (Department of Statistics, Iowa State University) ;
  • Yang, Yuhong (School of Statistics, University of Minnesota)
  • Published : 2005.12.01
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Abstract

Among different regression approaches, nonparametric procedures perform well under different conditions. In practice it is very hard to identify which is the best procedure for the data at hand, thus model combination is of practical importance. In this paper, we focus on one dimensional regression with fixed design. Polynomial regression, local regression, and smoothing spline are considered. The data are split into two parts, one part is used for estimation and the other part is used for prediction. Prediction performances are used to assign weights to different regression procedures. Simulation results show that the combined estimator performs better or similarly compared with the estimator chosen by cross validation. The combined estimator generates a similar risk to the best candidate procedure for the data.

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References

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  1. Arm using individual estimator for variance vol.21, pp.1-2, 2006, https://doi.org/10.1007/BF02896421