Estimating regression models with unknown break‐points
Top Cited Papers
- 8 September 2003
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 22 (19) , 3055-3071
- https://doi.org/10.1002/sim.1545
Abstract
This paper deals with fitting piecewise terms in regression models where one or more break‐points are true parameters of the model. For estimation, a simple linearization technique is called for, taking advantage of the linear formulation of the problem. As a result, the method is suitable for any regression model with linear predictor and so current software can be used; threshold modelling as function of explanatory variables is also allowed. Differences between the other procedures available are shown and relative merits discussed. Simulations and two examples are presented to illustrate the method. Copyright © 2003 John Wiley & Sons, Ltd.Keywords
This publication has 23 references indexed in Scilit:
- Models for the estimation of a ‘no effect concentration’Environmetrics, 2002
- Bayesian analysis of logistic regression with an unknown change point and covariate measurement errorStatistics in Medicine, 2001
- Inference and Estimation in a Changepoint Regression ProblemJournal of the Royal Statistical Society: Series D (The Statistician), 2001
- Threshold Models for Combination Data from Reproductive and Developmental ExperimentsJournal of the American Statistical Association, 1995
- Testing for Two-Phase RegressionsTechnometrics, 1979
- Some Algorithms for Linear Spline and Piecewise Multiple Linear RegressionJournal of the American Statistical Association, 1976
- Point Estimation of the Parameters of Piecewise Regression ModelsJournal of the Royal Statistical Society Series C: Applied Statistics, 1976
- Estimating the transition between two intersecting straight linesBiometrika, 1971
- Transformation of the Independent VariablesTechnometrics, 1962