Selected recent publications:<\/p>\n
Predictive Insights, From Linear Regression to Machine Learning<\/i> (mongraph\/professional book), Chapman and Hall, to appear 2016<\/p>\n
A Closer Look at What We Should Mean by “Big” in “Big Data,”\u00a0Handbook of Big Data<\/i>\u00a0(invited book chapter<\/b>), Hans Buhlmann and Michael Kane (eds.), Chapman and Hall, to appear 2015<\/p>\n
Improved Estimation of Class Probabilities through Unlabeled Data, arXiv:1510.01422, 2015<\/p>\n
Parallel Computation for Data Science<\/em> (professional book), Chapman and Hall, 2015<\/p>\n A Different Approach to the Problem of Missing Data, with Xiao Gu, JSM <\/i>2015<\/p>\n A New Approach to the Parallel Coordinates Method for Large Data Sets, with Yingkang Xie, JSM 2014<\/i><\/p>\n Long Live (Big Data-fied) Statistics!,\u00a0invited paper<\/b>, JSM 2013<\/i>, 98-108<\/p>\n Efficient Parallel R Loops on Long-Latency Platforms,\u00a0invited paper<\/b>,\u00a0Proceedings of Interface 201<\/i>2– Future of Statistical Computing: Internet Scale Data, Flexible Modeling, and Visualization\u00a0<\/i>, Scott, Wickham and Morris (eds.), 2012<\/p>\n The Art of R Programming<\/i>\u00a0(professional book), No Starch Press, 2011<\/p>\n A New Method for Rule Finding Via Bootstrapped Confidence Intervals.\u00a0SIAM Conference on Data Mining<\/i>, 2008, 547-552<\/p>\n Using Soft-line Recursive Response to Improve Query Aggregation in Wireless Sensor Networks (with X. Lu, M. Spear, K. Levitt, F. Wu). 2008 IEEE International Conference on Communications, <\/i> 2309-2316<\/p>\n Availability-Aware Provisioning Strategies for Differentiated Protection Services in Wavelength-Convertible WDM Mesh Networks (with B. Mukherjee, H. Zang, J. Zhang and K. Zhu). IEEE\/ACM Transactions on Networking, <\/i> 2007, 15, 5, 1177-1190.<\/p>\n Estimation of Internet File-Access\/Modification Rates, ACM Transactions on Modeling and Computer Simulation, <\/i> 2005, 15, 3, 233-253.<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Selected recent publications: Predictive Insights, From Linear Regression to Machine Learning (mongraph\/professional book), Chapman and Hall, to appear 2016 A Closer Look at What We Should Mean by “Big” in “Big Data,”\u00a0Handbook of Big Data\u00a0(invited book chapter), Hans Buhlmann and Michael Kane (eds.), Chapman and Hall, to appear 2015 Improved \u2026 Continue reading