Talk One：Asymmetric Conditional Correlations in Stock Returns
Speaker：Prof. Yingcun Xia
Time： am 9:00, 2016.6.24，Room：X2511
Abstract: Modeling and estimation of correlation coefficient is a fundamental step in risk management, especially with the aftermath of the financial crisis in 2008, which challenged the traditional measuring of dependence in financial market. Because of the serial dependence and small signal-to-noise ratio, patterns of the dependence in the data cannot be easily detected and modeled. This paper introduces a common factor analysis into the conditional correlation coefficients to extract the features of dependence. While statistical properties are thoroughly derived, extensive empirical analysis provides us with common patterns for the conditional correlation coefficients that give new insight into a number of important questions in financial data, especially the asymmetry of cross-correlations and the factors that drive the cross-correlations.
Talk Two：Classification with Ultrahigh-Dimensional Features
Speaker：Prof. Yi Li
Time： am 10:00, 2016.6.24，Room：X2511
Abstract: Although much progress has been made in classification with high-dimensional features, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work.
This paper introduces a novel and computationally feasible multivariate screening and classification method for ultrahigh-dimensional data. Leveraging inter-feature correlations, the proposed method enables detection of marginally weak and sparse signals and recovery of the true informative feature set, and achieves asymptotic optimal misclassification rates.We also show that the proposed procedure provides more powerful discovery boundaries compared to those in Cai and Sun (2014) and Jin et al. (2009). The performance of the proposed procedure is evaluated using simulation studies and demonstrated via classification of patients with different post-transplantation renal functional types.
Talk Three：A nonparametric approach for partial areas under ROC curves
Speaker：Prof. Yichuan Zhao
Time： am 11:00, 2016.6.24，Room：X2511
Abstract: The receiver operating characteristic (ROC) curve is a well-known measure of the performance of a classification method. Interest may only pertain to a specific region of the curve and, in this case, the partial area under the ROC curve (pAUC) provides a useful summary measure.Related measures such as the ordinal dominance curve (ODC) and the partial area under the ODC (pODC) are frequently of interest as well. Based on a novel estimator of pAUC proposed by Wang and Chang (2011), we develop nonparametric approaches to the pAUC and pODC using normal approximation, the jackknife and the jackknife empirical likelihood. A simulation study demonstrates the flaws of the existing method and shows proposed methods perform well. Simulations also substantiate the consistency of our jackknife variance estimator. The Pancreatic Cancer Serum Biomarker data set is used to illustrate the proposed methods.
