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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 제7권 제1호
- 발행연도
- 2005.1
- 수록면
- 61 - 71 (11page)
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The main purpose of this paper is to make a connection between two regression methods which seemingly appear to be quite different: partial least squares regression (PLSR) and generalized ridge regression (GRR). The former uses latent factors extracted from both and and appears as an iterative algorithm in most literatures, and the latter adds small quantities to the diagonal of prior to inversion to reduce the variance of estimated regression coefficients. We connect the two methods to develop a new regression solution. This can be done by modifying PLSR expression in an approximate form and relating it to GRR expression, which results in a new type of estimates of ridge parameters. An example is given to illustrate the above method and its predictive ability is compared with other common regression methods.
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참고문헌 (9)
참고문헌 신청
/ 1970 / Biased Estimation for Nonorthogonal Problems tech (metrics) : 55 ~ 67
/ 1975 / Communications in Statistics : 105 ~ 123
/ 1983 / A Misuse of Ridge Regression in the Calibration of a Near Infrared Reflectance Instrument 32 : 73 ~ 79
/ 1985 / Encyclopedia of Statistical Sciences : 581 ~ 591
/ 1988 / On the structure of partial least squares regression 17 : 581 ~ 607
/ 1975 / Communications in Statistics : 105 ~ 123
/ 1983 / A Misuse of Ridge Regression in the Calibration of a Near Infrared Reflectance Instrument 32 : 73 ~ 79
/ 1985 / Encyclopedia of Statistical Sciences : 581 ~ 591
/ 1988 / On the structure of partial least squares regression 17 : 581 ~ 607
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