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Estimating regression models with unknown break‐points

Statistics in Medicine · 2003 · Vol. 22(19) · pp. 3055–3071
Vito M. R. Muggeo

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.

Advanced Statistical Methods and ModelsStatistical Methods and InferenceStatistical Methods and Bayesian InferenceProper linear modelLinearizationSegmented regressionRegression diagnosticRegression analysisLinear regressionSimple (philosophy)Computer scienceRegressionLinear model

MeSH terms

BronchitisChronic DiseaseDustHumansRegression AnalysisModels, StatisticalSurvival AnalysisHeart Transplantation
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References
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Biometrics · 1991 · 8,286 citations
Generalized Additive Models.
Journal of the American Statistical Association · 1991 · 7,687 citations
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