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.

10.7717/peerj.2111 http://scihub22266oqcxt.onion/10.7717/peerj.2111 C4906649!4906649 !27330866     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27330866 &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       PeerJ 2016 ; 4 (?): e2111 Nephropedia Template TP {{tp|p=27330866 |t=2016. On causality of extreme events |pdf=|usr=}}{{27330866 }} gab.com Text On causality of extreme events JO: PeerJ http://www.kidney.de/pget.php?&tw=1&pmid=27330866 #FOAvip Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available. Twit Text FOAVip On causality of extreme events ????PeerJ http://www.kidney.de/pget.php?&tw=1&pmid=27330866 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27330866 #FOAvip English Wikipedia <ref>{{Cite pmid|27330866 }}</ref> On causality of extreme events #MMPMID27330866 Zanin M PeerJ 2016[]; 4 (?): e2111 PMID27330866 show ga Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available. ?On causality of extreme events (epmc).pdf DeepDyve Pubget Overpricing