【摘 要】
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The analysis of high-dimensional data sets now commonly arising in scientific inves-tigations poses many statistical challenges not present in smaller scale
【机 构】
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UniversityofPennsylvania3730WalnutSt.400JMHH/WhartonSchoolPhiladelphia,PA19104
【出 处】
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The Third IMS-China International Conference on Statistics a
论文部分内容阅读
The analysis of high-dimensional data sets now commonly arising in scientific inves-tigations poses many statistical challenges not present in smaller scale studies.Among the many interesting applications are genomics, fMRI analysis, risk management, and web search problems.In the high dimensional setting where the dimension p can be much larger than the sample size n, classical methods and results based on fixed p and large n are no longer applicable.In this talk, I will discuss some recent results on optimal estimation of large covariance/precision matrices as well as sparse linear discriminant analysis with high-dimensional data.The results and technical analysis reveal new features that are quite different from the conventional low-dimensional problems.
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