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  • 统计与管理学院2017年学术报告第22

     

    【主  题】Adaptive Basis Sampling for Exponential Family Smoothing Splines with Application in Sequencing Data Modeling

    【报告人】张楠, 副研究员

    复旦大学大数据学院

    【时  间】 2017年05月03日(星期三)16:00-17:00

    【地  点】 上海财经大学统计与管理学院大楼1208室

    【摘  要】Second-generation sequencing technologies have become the default method for genomics and epigenomics analysis. Second-generation sequencing technologies sequence tens of millions of DNA/cDNA fragments in parallel and one gets a sequence of short read counts along the genome. Effective extraction of signals in these short read counts is the key to the success of sequencing technologies. Nonparametric methods, in particular, smoothing splines, have been used extensively for modeling and processing single sequencing sample. However, nonparametric joint modeling of multiple second-generation sequencing samples is still lacking due to expensive computational cost. To achieve a scalable computation for large data sets with multivariate covariates, we develop an adaptive sampling method to select basis functions and construct a low-dimensional approximation of the estimates. Our asymptotic analysis shows such approximation converges to the true function at the same rate as full basis smoothing spline estimator. The empirical performance is demonstrated through both simulation studies and second-generation sequencing data examples.

    【邀请人】 柏杨、黄涛