Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1073/pnas.1518285112 http://scihub22266oqcxt.onion/10.1073/pnas.1518285112 C4653162!4653162!26504198     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26504198&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 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+Natl+Acad+Sci+U+S+A 2015 ; 112 (45): 13892-7 Nephropedia Template TP {{tp|p=26504198|t=2015. Why significant variables aren?t automatically good predictors |pdf=|usr=}}{{26504198}} gab.com Text Why significant variables aren t automatically good predictors JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=26504198 #FOAvip A recent puzzle in the big data scientific literature is that an increase in explanatory variables found to be significantly correlated with an outcome variable does not necessarily lead to improvements in prediction. This problem occurs in both simple and complex data. We offer explanations and statistical insights into why higher significance does not automatically imply stronger predictivity and why variables with strong predictivity sometimes fail to be significant. We suggest shifting the research agenda toward searching for a criterion to locate highly predictive variables rather than highly significant variables. We offer an alternative approach, the partition retention method, which was effective in reducing prediction error from 30% to 8% on a long-studied breast cancer data set. Twit Text FOAVip Why significant variables aren t automatically good predictors ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=26504198 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26504198 #FOAvip English Wikipedia <ref>{{Cite pmid|26504198}}</ref> Why significant variables aren?t automatically good predictors #MMPMID26504198 Lo A; Chernoff H; Zheng T; Lo SH Proc Natl Acad Sci U S A 2015[Nov]; 112 (45): 13892-7 PMID26504198show ga A recent puzzle in the big data scientific literature is that an increase in explanatory variables found to be significantly correlated with an outcome variable does not necessarily lead to improvements in prediction. This problem occurs in both simple and complex data. We offer explanations and statistical insights into why higher significance does not automatically imply stronger predictivity and why variables with strong predictivity sometimes fail to be significant. We suggest shifting the research agenda toward searching for a criterion to locate highly predictive variables rather than highly significant variables. We offer an alternative approach, the partition retention method, which was effective in reducing prediction error from 30% to 8% on a long-studied breast cancer data set. äWhy significant variables aren?t automatically good predictors (epmc).pdf DeepDyve Pubget Overpricing