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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제11권 제4호
- 발행연도
- 2009.1
- 수록면
- 1,761 - 1,769 (9page)
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Recently, it showed that the kernel type estimates based on the fundamental cardinal splines were asymptotically optimal for certain functional classes. In order to show it, the asymptotically optimal bandwidth which depend on the parameters of given functional class was introduced. However, in practice, the parameters are hardly ever exactly known. So, a practical and appropriate bandwidth should be driven from the data. In this paper we consider the generalized cross validation(GCV) criterion as a method for selecting a bandwidth. We provide the simple formula of GCV criterion in fundamental cardinal spline estimates. In addition, a numerical example is given for illustrating the usefulness of GCV criterion in fundamental cardinal spline estimates.
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참고문헌 (8)
참고문헌 신청
Cho, J / 2008 / Cardinal Splines in Nonparametric Regression / Mathematical Methods of Statistics 17 (1) : 19 ~ 34
DongryeonPark / 2003 / Comparison of Local Constant Regression and Local Linear Regression in the Small Sample Situation / Journal of The Korean Data Analysis Society 5 (4) :
Eubank, R. L / 1999 / Spline Smoothing and Nonparametric Regression, 2nd edition / Marcel Dekker
Jong-DukKim / 2002 / A Simulation Study on the Prediction Ability of Biased Regression Methods / Journal of The Korean Data Analysis Society 4 (4) : 361 ~ 372
Lepski, O / 1998 / Adaptive Minimax Estimation of Infinitely Differentiable Functions / Mathematical Method of Statistics 7 : 123 ~ 156
DongryeonPark / 2003 / Comparison of Local Constant Regression and Local Linear Regression in the Small Sample Situation / Journal of The Korean Data Analysis Society 5 (4) :
Eubank, R. L / 1999 / Spline Smoothing and Nonparametric Regression, 2nd edition / Marcel Dekker
Jong-DukKim / 2002 / A Simulation Study on the Prediction Ability of Biased Regression Methods / Journal of The Korean Data Analysis Society 4 (4) : 361 ~ 372
Lepski, O / 1998 / Adaptive Minimax Estimation of Infinitely Differentiable Functions / Mathematical Method of Statistics 7 : 123 ~ 156
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