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10.1214/16-STS604 http://scihub22266oqcxt.onion/10.1214/16-STS604 C5584669!5584669 !28883686     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28883686 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28883686 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Stat+Sci 2017 ; 32 (3 ): 385-404 Nephropedia Template TP {{tp|p=28883686 |t=2017. Principles of Experimental Design for Big Data Analysis |pdf=|usr=}}{{28883686 }} gab.com Text Principles of Experimental Design for Big Data Analysis JO: Stat Sci http://www.kidney.de/pget.php?&tw=1&pmid=28883686 #FOAvip Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality. The purpose of this paper is to open a discourse on the potential for modern decision theoretic optimal experimental design methods, which by their very nature have traditionally been applied prospectively, to improve the analysis of Big Data through retrospective designed sampling in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has the potential for wide generality and advantageous inferential and computational properties. We highlight current hurdles and open research questions surrounding efficient computational optimisation in using retrospective designs, and in part this paper is a call to the optimisation and experimental design communities to work together in the field of Big Data analysis. Twit Text FOAVip Principles of Experimental Design for Big Data Analysis ????Stat Sci http://www.kidney.de/pget.php?&tw=1&pmid=28883686 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28883686 #FOAvip English Wikipedia <ref>{{Cite pmid|28883686 }}</ref> Principles of Experimental Design for Big Data Analysis #MMPMID28883686 Drovandi CC ; Holmes C ; McGree JM ; Mengersen K ; Richardson S ; Ryan EG Stat Sci 2017[Aug]; 32 (3 ): 385-404 PMID28883686 show ga Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality. The purpose of this paper is to open a discourse on the potential for modern decision theoretic optimal experimental design methods, which by their very nature have traditionally been applied prospectively, to improve the analysis of Big Data through retrospective designed sampling in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has the potential for wide generality and advantageous inferential and computational properties. We highlight current hurdles and open research questions surrounding efficient computational optimisation in using retrospective designs, and in part this paper is a call to the optimisation and experimental design communities to work together in the field of Big Data analysis. äPrinciples of Experimental Design for Big Data Analysis (epmc).pdf DeepDyve Pubget Overpricing