STATISTICS COLLOQUIUM
Taeryon Choi
Korea University
Bayesian shape restricted regression models using
Gaussian processes priors
ABSTRACT
In this talk, we propose a Bayesian method for shape-restricted regression using a spectral analysis of Gaussian process priors for the regression function. The proposed model directly enforces shape-restrictions on the derivatives of the regression function. The smoothing prior distribution for the spectral coe cients incorporates hyper parameters that control the smoothness of the function and the tradeo between the data and the prior distribution. We contrast our approach with existing Bayesian shape-restricted regression models for dealing with regression functions with monotonicity and concavity. We also propose models for U-shaped and S-shaped functions that facilitate the estimation of the extrema and in ection points. We modify the basic model with a slab and spike prior that improves model when the true function is on the boundary of the constraint space. The posterior distributions of the proposed models are consistent. We also examine Bayesian hypothesis testing for shape restrictions and discuss its potentials and limitations. We also illustrate the empirical performance of the proposed models with synthetic and real data and compare them with existing Bayesian methods.
DATE: Wednesday, September 7, 2016
TIME: 4:00 pm – 5:00 pm
PLACE: Philip E. Austin Bldg., Rm. 105
Coffee will be served at 3:30 in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu