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10.4310/SII.2016.v9.n4.a1 http://scihub22266oqcxt.onion/10.4310/SII.2016.v9.n4.a1 C5041595/?report=reader!5041595!27695593     free     free     free       Stat+Interface 2016 ; 9 (4): 399-414 Nephropedia Template TP {{tp|p=27695593|t=2016. Statistical methods and computing for big data |pdf=|usr=}}{{27695593}} gab.com Text Statistical methods and computing for big data JO: Stat Interface http://www.kidney.de/pget.php?&tw=1&pmid=27695593 #FOAvip Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard analytic tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article summarizes recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and online updating for stream data. As a new contribution, the online updating approach is extended to variable selection with commonly used criteria, and their performances are assessed in a simulation study with stream data. Software packages are summarized with focuses on the open source R and R packages, covering recent tools that help break the barriers of computer memory and computing power. Some of the tools are illustrated in a case study with a logistic regression for the chance of airline delay. Twit Text FOAVip Statistical methods and computing for big data ????Stat Interface http://www.kidney.de/pget.php?&tw=1&pmid=27695593 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27695593 #FOAvip English Wikipedia <ref>{{Cite pmid|27695593}}</ref> Statistical methods and computing for big data #MMPMID27695593 Wang C; Chen MH; Schifano E; Wu J; Yan J Stat Interface 2016[]; 9 (4): 399-414 PMID27695593show ga Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard analytic tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article summarizes recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and online updating for stream data. As a new contribution, the online updating approach is extended to variable selection with commonly used criteria, and their performances are assessed in a simulation study with stream data. Software packages are summarized with focuses on the open source R and R packages, covering recent tools that help break the barriers of computer memory and computing power. Some of the tools are illustrated in a case study with a logistic regression for the chance of airline delay. äStatistical methods and computing for big data (epmc).pdf DeepDyve Pubget Overpricing