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

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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.

Original language	English
Pages (from-to)	836-858
Number of pages	23
Journal	Computational Statistics and Data Analysis
Volume	51
Issue number	2
DOIs	https://doi.org/10.1016/j.csda.2005.09.006
State	Published - Nov 15 2006

Keywords

Nonlinear models
Partial least squares
Principal curves

ASJC Scopus subject areas

Statistics and Probability
Computational Mathematics
Computational Theory and Mathematics
Applied Mathematics

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10.1016/j.csda.2005.09.006

Cite this

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AU - Rayens, William S.

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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

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SP - 836

EP - 858

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 2

ER -

Reciprocal curves

Abstract

Keywords

ASJC Scopus subject areas

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