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Statistically and Numerically Efficient Independence Test

Prof Gang Li Department of Biostatistics and Biomathematics, University of Los Angeles at California

Date:08 June 2017, Thursday

Location:08:00am - 05:00pm

Time:S16-06-118, DSAP Seminar Room

 

Recent advances of medical informative tools and high throughput technologies have made large scale data routinely accessible to researchers, administrators, and policy-makers. Large scale data provides unprecedented opportunities for new and innovative approaches to science and medical research. On the other hand, this “data deluge” also poses new challenges and critical barriers for biostatisticians and scientists as existing statistical methods are rendered unfeasible for analyzing these large scale datasets. In this talk I will discuss a new suite of scalable sparse regression tools for large scale medical data. Specifically, we will focus on two common types of large scale data: 1) high dimensional (number of predictors in thousands, or tens of thousands), massive sample-size (thousands or tens of thousands) data; and 2) high dimensional (number of predictors in thousands, tens of thousands), small sample-size (tens or hundreds) data.