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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2011.12
- 수록면
- 1,223 - 1,232 (10page)
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Tong and Wang`s estimator (2005) is a new approach to estimate the error variance using least squares method such that a simple linear regression is asymptotically de-rived from Rice`s lag- estimator (1984). Their estimator highly depends on the setting of a regressor and weights in small sample sizes. In this article, we propose a new approach via a local quadratic approximation to set regressors in a small sample case. We estimate the error variance as the intercept using a ridge regression because the regressors have the problem of multicollinearity. From the small simulation study, the performance of our approach with some existing methods is better in small sample cases and comparable in large cases. More research is required on unequally spaced points.
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참고문헌 (10)
참고문헌 신청
1. [학술지(정기간행물)] - Dette, H. / 1998 / Estimating the variance in nonparametric regression- What is a reasonable choice / Journal of the Royal Statistical Society B 60 : 751 ~ 764
2. [학술지(정기간행물)] - Gasser, T. / 1986 / Residual variance and residual pattern in nonlinear regression / Biometrika 73 : 625 ~ 633
3. [학술지(정기간행물)] - Hall, P. / 1990 / Asymptotically optimal difference-based estimator of variance in nonparametric regression / Biometrika 77 : 521 ~ 528
4. [학술지(정기간행물)] - Hoerl, A. E / 1970 / Ridge regression: Biased estimation for nonorthogonal problems / Technometrics 12 : 55 ~ 67
5. [학술지(정기간행물)] - Hoerl, A. E. / 1970 / Ridge regression: Biased estimation for nonorthogonal problems / Technometrics 12 : 69 ~ 82
2. [학술지(정기간행물)] - Gasser, T. / 1986 / Residual variance and residual pattern in nonlinear regression / Biometrika 73 : 625 ~ 633
3. [학술지(정기간행물)] - Hall, P. / 1990 / Asymptotically optimal difference-based estimator of variance in nonparametric regression / Biometrika 77 : 521 ~ 528
4. [학술지(정기간행물)] - Hoerl, A. E / 1970 / Ridge regression: Biased estimation for nonorthogonal problems / Technometrics 12 : 55 ~ 67
5. [학술지(정기간행물)] - Hoerl, A. E. / 1970 / Ridge regression: Biased estimation for nonorthogonal problems / Technometrics 12 : 69 ~ 82
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UCI(KEPA) : I410-ECN-0101-2018-041-001381631