報告題目：Quantile based data analysis methods for forecast, classification,clustering and Bayesian inference
報告人：Keming Yu，the Chair in Statistics at Brunel University London
報告簡介: While information and data are widely available nowadays, data analysis accuracy is required if classical methods suffer problems. In this talk we introduce some recent advance in Quantile forecasting, quantile classification, quantile clustering, quantile Bayesian regression and quantile random forest.
Professor Keming Yu is the Chair in Statistics at Brunel University London. He is the Fellow of the Royal Statistics Society, Associate Editor of the Royal Statistics Society-A and the Associate Editor of Statistics with Its Interface. His research interests cover small data analysis, big data analysis, quantile regression, Statistical Learning, reliability engineering, financial econometrics, risk analysis. He has published more than 130 papers. It’s well known that he has pioneered the Bayesian quantile regression models and methods . And he has successfully supervised more than 20 PhD students!