Abstract
Different nonparametric procedures in regression analysis perform well under different conditions. A combining method, adaptive regression by mixing (ARM), was proposed for random design. ARM was introduced as well in case of the fixed design (ARMC). In this article, we focus on the individual estimate for variance in ARMI algorithm. Prediction performance and individual\(\hat \sigma ^2 \) need to be estimated in order to assign weight to different procedures. Simulation results show that the ARMI performs better or similarly compared to CV estimator.
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References
W. S. Cleveland,Robust Locally Weighted Regression and Smoothing Scatterplots, Journal of the American Statistical Association,74 (1979), 829–836
R. L. Eubank,n Spline Smoothing and Nonparametric Regression, 1st edition, Marcel Dekker, Inc., 1988
J. C. Oh, Y. Lu and Y. Yang,Adaptive Regression by Mixing for Fixed Design, The Korean Communications in Statistics,12 (2005), 713–727
C. J. Stone,Consistent Nonparametric Regression, Annals of Statistics,5 (1977), 595–620
Y. Yang,Adaptive Regression by Mixing, JASA,96 (2001), 574–588
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Oh, JC., Lu, Y. & Yang, Y. Arm using individual estimator for variance. J. Appl. Math. Comput. 21, 477–483 (2006). https://doi.org/10.1007/BF02896421
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DOI: https://doi.org/10.1007/BF02896421