Predicting Y from a future X based on data (Xi, Yi) is a fundamental inference problem. When X is observed accurately, the problem is that of standard regression estimation of E(Y |X). When the data Xi and future X are measured with error, prediction is sometimes less standard. With W denoting the future X measurement, prediction of Y requires estimation of E(Y |W). This is complicated when measurements are made under different conditions, so that errors in Xi and X are not identically distributed. We study this problem nonparametrically showing that convergence rates of estimators of E(Y |W) can vary from root-n to much slower nonparametric rates. We develop highly-adaptive, data-driven methods that perform well as illustrated by an interesting application in nutritional epidemiology.


About the speaker: Aurore Delaigle is Associate Professor at the Department of Mathematics and Statistics of the University of Melbourne. She currently holds a Queen Elizabeth II Fellowship and concentrates on her research on deconvolution problems, functional data analysis and high dimensional problems.


Associate Professor Aurore Delaigle

Research Area

Statistics Seminar


University of Melbourne


Fri, 26/03/2010 - 4:00pm