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10.1111/rssb.12131 http://scihub22266oqcxt.onion/10.1111/rssb.12131 C4996506!4996506!27570475     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+R+Stat+Soc+Series+B+Stat+Methodol 2016 ; 78 (4): 781-804 Nephropedia Template TP {{tp|p=27570475|t=2016. Making the cut: improved ranking and selection for large?scale inference |pdf=|usr=}}{{27570475}} gab.com Text Making the cut: improved ranking and selection for large scale inference JO: J R Stat Soc Series B Stat Methodol http://www.kidney.de/pget.php?&tw=1&pmid=27570475 #FOAvip Identifying leading measurement units from a large collection is a common inference task in various domains of large?scale inference. Testing approaches, which measure evidence against a null hypothesis rather than effect magnitude, tend to overpopulate lists of leading units with those associated with low measurement error. By contrast, local maximum likelihood approaches tend to favour units with high measurement error. Available Bayesian and empirical Bayesian approaches rely on specialized loss functions that result in similar deficiencies. We describe and evaluate a generic empirical Bayesian ranking procedure that populates the list of top units in a way that maximizes the expected overlap between the true and reported top lists for all list sizes. The procedure relates unit?specific posterior upper tail probabilities with their empirical distribution to yield a ranking variable. It discounts high variance units less than popular non?maximum?likelihood methods and thus achieves improved operating characteristics in the models considered. Twit Text FOAVip Making the cut: improved ranking and selection for large scale inference ????J R Stat Soc Series B Stat Methodol http://www.kidney.de/pget.php?&tw=1&pmid=27570475 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27570475 #FOAvip English Wikipedia <ref>{{Cite pmid|27570475}}</ref> Making the cut: improved ranking and selection for large?scale inference #MMPMID27570475 Henderson NC; Newton MA J R Stat Soc Series B Stat Methodol 2016[Sep]; 78 (4): 781-804 PMID27570475show ga Identifying leading measurement units from a large collection is a common inference task in various domains of large?scale inference. Testing approaches, which measure evidence against a null hypothesis rather than effect magnitude, tend to overpopulate lists of leading units with those associated with low measurement error. By contrast, local maximum likelihood approaches tend to favour units with high measurement error. Available Bayesian and empirical Bayesian approaches rely on specialized loss functions that result in similar deficiencies. We describe and evaluate a generic empirical Bayesian ranking procedure that populates the list of top units in a way that maximizes the expected overlap between the true and reported top lists for all list sizes. The procedure relates unit?specific posterior upper tail probabilities with their empirical distribution to yield a ranking variable. It discounts high variance units less than popular non?maximum?likelihood methods and thus achieves improved operating characteristics in the models considered. äMaking the cut: improved ranking and selection for large?scale inferen(epmc).pdf DeepDyve Pubget Overpricing