Statistics Colloquium: STA 290
TUESDAY, November 26th, 2013 at 4:10pm, MSB 1147 (Colloquium Room)
Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)
Speaker: DIMITRIS POLITIS University of California, San Diego
Title: "Model-free prediction intervals for regression and autoregression"
Abstract: The bootstrap is an invaluable tool for prediction intervals without having to assume normality of the data. However, even when all other model assumptions are correctly specified, bootstrap prediction intervals for regression (and autoregression) are well-known to be plagued by undercoverage; this is true even in the simplest case of linear regression. Furthermore, it can be the case that model assumptions are violated in which case any model-based inference will be invalid.
In this talk, the problem of statistical prediction is revisited with a view that goes beyond the typical parametric/nonparametric dilemmas in order to reach a fully model-free environment for predictive inference, i.e., point predictors and predictive intervals. The `Model-Free (MF) Prediction Principle' of Politis (2013) is based on the notion of transforming a given set-up into one that is easier to work with, namely i.i.d. or Gaussian. The two important applications are regression and autoregression whether an additive parametric/nonparametric model is applicable or not.
Reference: D. N. Politis (2013). `Model-free Model-fitting and Predictive Distributions', Invited Discussion paper in journal TEST, vol. 22, no. 2, pp. 183-221.