Segmented regression is a type of nonlinear regression that allows differing functional forms to be fit over different ranges of the explanatory variable. This paper considers the simple segmented regression case of two linear segments that are constrained to meet, often called the linear-linear model. Parameter estimation in the case where the joinpoint between the regimes is unknown can be tricky. Using a simulation study, four estimators for the parameters of the linear-linear model are evaluated. The bias and mean squared error of the estimators are considered under differing parameter combinations and sample sizes. Parameters estimated in the model are the location of the change-point, the slope and intercept of the first segment, the change in slope from the first segment to the second, and the variance over both segments.
College and Department
Physical and Mathematical Sciences; Statistics
BYU ScholarsArchive Citation
Hernandez, Erika Lyn, "Parameter Estimation in Linear-Linear Segmented Regression" (2010). All Theses and Dissertations. 2113.
change-point regression, broken-stick model, maximum likelihood, Bayesian estimators, bent-cable regression