Reciprocal curves

John E. Hinkle; William S. Rayens

doi:10.1016/j.csda.2005.09.006

Reciprocal curves

John E. Hinkle
, William S. Rayens

Dr. Bing Zhang Department of Statistics

Producción científica: Article › revisión exhaustiva

1 Cita (Scopus)

Resumen

Partial least squares (PLS) is a modeling technique that has met with considerable success, particularly in the fields of chemometrics and psychometrics. In this paper, we extend linear PLS to a nonlinear version called "reciprocal curves." Reciprocal curves are smooth one-dimensional curves that pass through the center of the data, are co-consistent and share several optimality properties originally identified with PLS.

Idioma original	English
Páginas (desde-hasta)	836-858
Número de páginas	23
Publicación	Computational Statistics and Data Analysis
Volumen	51
N.º	2
DOI	https://doi.org/10.1016/j.csda.2005.09.006
Estado	Published - nov 15 2006

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

Acceder al documento

10.1016/j.csda.2005.09.006

Otros archivos y enlaces

Citar esto

@article{f347112896a646c7b376e1d2455c9a76,

title = "Reciprocal curves",

abstract = "Partial least squares (PLS) is a modeling technique that has met with considerable success, particularly in the fields of chemometrics and psychometrics. In this paper, we extend linear PLS to a nonlinear version called {"}reciprocal curves.{"} Reciprocal curves are smooth one-dimensional curves that pass through the center of the data, are co-consistent and share several optimality properties originally identified with PLS.",

keywords = "Nonlinear models, Partial least squares, Principal curves",

author = "Hinkle, \{John E.\} and Rayens, \{William S.\}",

year = "2006",

month = nov,

day = "15",

doi = "10.1016/j.csda.2005.09.006",

language = "English",

volume = "51",

pages = "836--858",

number = "2",

}

TY - JOUR

T1 - Reciprocal curves

AU - Hinkle, John E.

AU - Rayens, William S.

PY - 2006/11/15

Y1 - 2006/11/15

N2 - Partial least squares (PLS) is a modeling technique that has met with considerable success, particularly in the fields of chemometrics and psychometrics. In this paper, we extend linear PLS to a nonlinear version called "reciprocal curves." Reciprocal curves are smooth one-dimensional curves that pass through the center of the data, are co-consistent and share several optimality properties originally identified with PLS.

AB - Partial least squares (PLS) is a modeling technique that has met with considerable success, particularly in the fields of chemometrics and psychometrics. In this paper, we extend linear PLS to a nonlinear version called "reciprocal curves." Reciprocal curves are smooth one-dimensional curves that pass through the center of the data, are co-consistent and share several optimality properties originally identified with PLS.

KW - Nonlinear models

KW - Partial least squares

KW - Principal curves

UR - https://www.scopus.com/pages/publications/33750335738

UR - https://www.scopus.com/pages/publications/33750335738#tab=citedBy

U2 - 10.1016/j.csda.2005.09.006

DO - 10.1016/j.csda.2005.09.006

M3 - Article

AN - SCOPUS:33750335738

SN - 0167-9473

VL - 51

SP - 836

EP - 858

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 2

ER -

Reciprocal curves

Resumen

ASJC Scopus subject areas

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto