Studies on choosing the bandwidth in a kernel regression estimation = Kernel 회귀 추정에서 bandwidth의 선택에 관한 연구

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 205
  • Download : 0
DC FieldValueLanguage
dc.contributor.advisorKim, Byung-Chun-
dc.contributor.advisor김병천-
dc.contributor.authorOh, Jong-Chul-
dc.contributor.author오종철-
dc.date.accessioned2011-12-14T04:38:18Z-
dc.date.available2011-12-14T04:38:18Z-
dc.date.issued1995-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=101844&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/41774-
dc.description학위논문(박사) - 한국과학기술원 : 수학과, 1995.8, [ [ii], 60 p. ]-
dc.description.abstractKernel density estimators have become quite popular in recent years, both as theoretical and practical enterprises, for detecting and displaying distributional structure in populations and ongoing research, mainly concerned with the data-driven choice of the bandwidth h, has made this methodology increasingly more practical. Simultaneously with the kernel density estimators nonparametric regression estimation has become a prominent statistical research topic as a useful tool. Like the discussion of the nonparametric kernel density, the applications of kernel regression always require a crucial choice of bandwidth. Hence various methods have been developed for data-based procedure for choosing the optimal bandwidth. In Chapter 2, we consider kernel density estimation, least-squares cross-validation (CV) and biased cross-validation (BCV). These methods are applied to kernel regression estimation. We proposed, in Chapter 3, the biased cross-validation bandwidth selector BCV. Rice (1984) discussed the relationship of various nonparametric kernel regression bandwidth selectors. Later, in simulations done by Hardle et al. (1988) the selectors, discussed by Rice, performed quite differently from each other. We show that the bandwidth chosen by BCV method proposed in this thesis is optimal in the sense of the asymptotical mean average squared error criterion, and has small sample variability. The simulation results verify that when the underlying regression is sufficiently smooth, the proposed bandwidth is closer to optimal bandwidth $h_m$ (or $h^*$). Because of the assumptions of the underlying regression, there is still room for improvement, but the proposed BCV bandwidth has best performance than other selectors.eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subject커널 회귀 곡선-
dc.subject척도 모수-
dc.subject모의 실험-
dc.subjectKernel density estimation-
dc.subject커널 밀도 추정-
dc.subjectBCV-
dc.subjectKernel regression-
dc.subjectBandwidth-
dc.subjectCV-
dc.titleStudies on choosing the bandwidth in a kernel regression estimation = Kernel 회귀 추정에서 bandwidth의 선택에 관한 연구-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN101844/325007-
dc.description.department한국과학기술원 : 수학과, -
dc.identifier.uid000875249-
dc.contributor.localauthorKim, Byung-Chun-
dc.contributor.localauthor김병천-
Appears in Collection
MA-Theses_Ph.D.(박사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0