Multivariate regression splines

Lin-Ann Chen

doi:10.1016/S0167-9473(97)00018-2

Multivariate regression splines

Lin-Ann Chen

Institute of Statistics

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

3 Scopus citations

Abstract

Multivariate regression splines of arbitrary order assuming known knots using the additional function are developed. Model description and possible parameter tests for obtaining regression splines are also stated in detail for the triplicate case. A simulation study of drawn data from the Lubricant nonlinear regression model, where “Lubricant” is referred to lubricant data that we use in application, to compare the mean squares errors for the multivariate regression spline models and the multivariate polynomial
models show the need of employing the multivariate regression splines in the approximation of the nonlinear regression models.

Original language	English
Pages (from-to)	71-82
Number of pages	12
Journal	Computational Statistics and Data Analysis
Volume	26
Issue number	1
DOIs	https://doi.org/10.1016/S0167-9473(97)00018-2
State	Published - 6 Nov 1997

Keywords

multivariate regression spline
piecewise polynomial

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10.1016/S0167-9473(97)00018-2

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title = "Multivariate regression splines",

abstract = "Multivariate regression splines of arbitrary order assuming known knots using the additional function are developed. Model description and possible parameter tests for obtaining regression splines are also stated in detail for the triplicate case. A simulation study of drawn data from the Lubricant nonlinear regression model, where “Lubricant” is referred to lubricant data that we use in application, to compare the mean squares errors for the multivariate regression spline models and the multivariate polynomialmodels show the need of employing the multivariate regression splines in the approximation of the nonlinear regression models. ",

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AB - Multivariate regression splines of arbitrary order assuming known knots using the additional function are developed. Model description and possible parameter tests for obtaining regression splines are also stated in detail for the triplicate case. A simulation study of drawn data from the Lubricant nonlinear regression model, where “Lubricant” is referred to lubricant data that we use in application, to compare the mean squares errors for the multivariate regression spline models and the multivariate polynomialmodels show the need of employing the multivariate regression splines in the approximation of the nonlinear regression models.

KW - multivariate regression spline

KW - piecewise polynomial

U2 - 10.1016/S0167-9473(97)00018-2

DO - 10.1016/S0167-9473(97)00018-2

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VL - 26

SP - 71

EP - 82

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

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ER -

Multivariate regression splines

Abstract

Keywords

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