【9月12日】bevictor伟德官网學術論壇
 

講座題目：Asymmetric AdaBoost for High Dimensional Maximum Score Regression

主 講 人：Tae Hwy Lee 教授

講座時間：2017年9月12日14:45—16:00

講座地點：bevictor伟德官网學術會堂606室

主講人簡介：

加州大學河濱分校經濟系教授，1990年6月畢業于加州大學聖地亞哥分校，獲得經濟學博士學位，導師為Halbert White Jr和諾貝爾經濟學獎獲得者Clive W.J. Granger先生。研究方向涵蓋時間序列、金融風險分析、大數據、機器學習等方面。為American Economic Review，Econometric Theory，Journal of Econometrics，Economics Letters等頂級期刊雜志匿名審稿人。獲得The Bank of Korea Research Award和Tjalling C. Koopmans Econometric Theory Prize等獎項。

Abstracts:

Adaptive Boosting or AdaBoost, introduced by Freund and Schapire (1996) has been proved to be effective to solve the high-dimensional binary classification or binary prediction problems. Friedman, Hastie, and Tibshirani (2000) show that AdaBoost builds an additive logistic regression model via mini- mizing the ‘exponential loss’. We show that the exponential loss in AdaBoost is equivalent (up to scale) to the symmetric maximum score (Manski 1975, 1985) and also to the symmetric least square loss for binary prediction. Therefore, the standard AdaBoost using the exponential loss is a symmetric algo- rithm and solves the binary median regression. In this paper, we introduce Asymmetric AdaBoost that produces an additive logistic regression model from minimizing the new ‘asymmetric exponential loss’ which we introduce in this paper. The Asymmetric AdaBoost can handle the asymmetric maximum score problem (Granger and Pesaran 2000, Lee and Yang 2006, Lahiri and Yang 2012, and Elliot and Lieli 2013) and therefore solve the binary quantile regression. We also show that our asymmetric ex- ponential loss is equivalent (up to scale) to the asymmetric least square loss (Newey and Powell 1987) for binary classification / prediction. We extend the result of Bartlett and Traskin (2007) and show that the Asymmetric AdaBoost algorithm is consistent in the sense that the risk of the classifier it produces approaches the Bayes Risk. Monte Carlo experiments show that Asymmetric AdaBoost performs well relative to the lasso-regularized high-dimensional logistic regression under various situations especially when p>>n and in the tails. We apply the Asymmetric AdaBoost to predict the business cycle turning points and directions of stock price changes.