Fitting segmented polynomial regression models whose join points have to be estimated

Andrew Ronald Gallant, Wayne A. Fuller

Research output: Contribution to journalArticle

164 Citations (Scopus)

Abstract

The study considers the problem of finding the least squares estimates for the unknown parameters of a regression model which consists of grafted polynomial submodels. The abscissae of the join points are a subset of the unknown parameters. Examples are given to illustrate how continuity and differentiability conditions on the model can be used to reparameterize the model so as to allow Modified Gauss-Newton fitting. A slightly generalized version of Hartley’s theorem is stated to extend the Modified Gauss-Newton method to this problem.

Original languageEnglish (US)
Pages (from-to)144-147
Number of pages4
JournalJournal of the American Statistical Association
Volume68
Issue number341
DOIs
StatePublished - Jan 1 1973

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Segmented Regression
Polynomial Regression
Polynomial Model
Unknown Parameters
Join
Regression Model
Gauss-Newton Method
Gauss-Newton
Least Squares Estimate
Differentiability
Polynomial
Subset
Theorem
Model
Regression model
Polynomial regression

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Fitting segmented polynomial regression models whose join points have to be estimated. / Gallant, Andrew Ronald; Fuller, Wayne A.

In: Journal of the American Statistical Association, Vol. 68, No. 341, 01.01.1973, p. 144-147.

Research output: Contribution to journalArticle

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