Wednesday, October 3, 2018 - 2:00pm - 2:45pm
Hamsa Bastani (Wharton School of the University of Pennsylvania)
Predictive analytics is increasingly used to guide decision-making in many applications. However, in practice, we often have limited data on the true outcome that we wish to predict, but copious data on an intermediate or proxy outcome. Practitioners often train predictive models on proxies since it achieves more accurate predictions.
Monday, April 23, 2018 - 1:30pm - 2:00pm
Ruey Tsay (University of Chicago)
In the last few years, an extensive literature has been focused on the ell-1 penalized least squares (Lasso) estimators of high dimensional linear regression when the number of covariates p is considerably larger than the sample size n. However, there is limited attention paid to the properties of the estimators when the errors or/and the covariates are serially dependent. In this study, we investigate the theoretical properties of the Lasso estimators for linear regression with random design under serially dependent and/or non-sub- Gaussian errors and covariates.
Friday, April 27, 2018 - 9:30am - 10:00am
Sumanta Basu (Cornell University)
The problem of learning interactions among the components of a large system from time series data is becoming increasingly common in many areas of biological and social sciences. Examples include learning regulatory interactions from time course gene expression data, understanding policy implications from a large number of macroeconomic time series, risk management and monitoring of financial institutions and exploring functional connections among different regions of human brain.
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