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dc.contributor.author곽분선-
dc.creator곽분선-
dc.date.accessioned2016-08-25T04:08:58Z-
dc.date.available2016-08-25T04:08:58Z-
dc.date.issued1989-
dc.identifier.otherOAK-000000022757-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/180580-
dc.identifier.urihttp://dcollection.ewha.ac.kr/jsp/common/DcLoOrgPer.jsp?sItemId=000000022757-
dc.description.abstractLeast squares estimates of the parameters in the usual linear regression model have a high probability of being unsatisfactory when the predictor variables are multicollinear. HOEAL and KENNARD have demonstrated that these undesirable effect of multicollinearity can be reduced by using ridge estimates in placed of the least squares estimates. Ridge regression theory in literature has been mainly concerned with selection of k. In this thesis, we demonstrate a simple selection rules of optimal k and compare this rule and the rules in literatures.;이 논문에서는 HOEAL 과 KENNARD 가 제시한 능형회귀추정량 (β(k))의 모수 k를 최적화 하는 방법을 TMSE 기준 하에서 제시하고, 이 방법과 문헌에 나와 있는 방법들을 예를 통해 비교하였다.-
dc.description.tableofcontentsABSTRACT = ⅰ CONTENTS = ⅱ LIST OF TABLES = ⅲ CHAPTER 1. INTRODUCTION = 1 CHAPTER 2. REVIEWS OF RIDGE ESTIMATORS = 6 CHAPTER 3. THE TOTAL MSE CRITERION AND RELATED THEOREMS = 10 CHAPTER 4. COMPARISON OF RIDGE SOLUTIONS = 15 CHAPTER 5. AN EXAMPLE = 18 REFERENCE = 23 논문초록-
dc.formatapplication/pdf-
dc.format.extent679173 bytes-
dc.languageeng-
dc.publisherThe Graduate School of Education at Ehwa Womans Univ.-
dc.subjectSELECTION RULES-
dc.subjectk IN RIDGE ESTIMATORS-
dc.subjectMathematics-
dc.titleA COMPARISON OF SELECTION RULES OF k IN RIDGE ESTIMATORS-
dc.typeMaster's Thesis-
dc.format.pageiii, 25 p.-
dc.identifier.thesisdegreeMaster-
dc.identifier.major대학원 수학과-
dc.date.awarded1990. 2-
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