Grace Chiu
University of Waterloo

Bent-Cable Regression with Autoregressive Noise  

Bent-cable regression extends the popular piecewise linear (broken-stick) model, allowing
for a smooth change region of any non-negative width. Existing bent-cable methodology
assumes independent and identically distributed errors. In this talk, we investigate data
that exhibit a bent-cable mean structure with noise generated by a low order
autoregressive model. We suggest a somewhat unusual approach for developing asymptotics
of both regression and time series parameter estimators. Preliminary results based on
simulations and physiological and atmospheric datasets are presented. This is joint work
with Prof. Richard Lockhart of Simon Fraser University.