Maseeh Mathematics + Statistics Colloquium: Fast grid search and bootstrap-based inference for two-phase polynomial regression
Friday, February 28, 2020 - 3:15pm

 The Maseeh Mathematics and Statistics Colloquium Series*



 Youyi Fong, Ph.D.

Fred Hutchinson Cancer Research Center



Fast grid search and bootstrap-based inference for

two-phase polynomial regression



Two-phase polynomial regression models (e.g. Robison, 1964; Fuller, 1969; Gallant and Fuller,1973; Zhan et al., 1996) are a generalization of two-segmented regression models (e.g. Hinkley 1971), in which the linear segments are replaced by polynomial functions. The point at which phase transition happens is often called threshold or change point and is estimated as part of the model. Such models are widely used in many applied fields today to model nonlinear relationships. Estimation of two-phase polynomial regression models is a non-convex, non-smooth optimization problem. Finding the true maximum likelihood estimator requires grid search, which is very slow if done in a brute force way. Following our previous works on two-segmented regression models estimation (Elder and Fong 2019), we develop fast grid search algorithms for threshold linear regression models with higher order trends and demonstrate their performance. We further develop model-robust confidence intervals for model parameters, as well as pointwise and simultaneous confidence bands for mean functions through Efron bootstrap. We conduct Monte Carlo studies to demonstrate the performance of the proposed methods, and illustrate the application of the models using several real datasets. This is joint work with Hyunju Son.



To read more about Dr. Fong’s areas of research, see his Google scholar profile.





Friday, February 28, 2020 at 3:15pm

Fariborz Maseeh Hall room B128
1855 SW Broadway

Light refreshments served


The faculty host of this speaker is Dr. Daniel Taylor-Rodriguez