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.

10.1080/00401706.2016.1142900 http://scihub22266oqcxt.onion/10.1080/00401706.2016.1142900 C5179229!5179229!28018007     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Technometrics 2016 ; 58 (3): 393-403 Nephropedia Template TP {{tp|p=28018007|t=2016. Online Updating of Statistical Inference in the Big Data Setting |pdf=|usr=}}{{28018007}} gab.com Text Online Updating of Statistical Inference in the Big Data Setting JO: Technometrics http://www.kidney.de/pget.php?&tw=1&pmid=28018007 #FOAvip We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting. Twit Text FOAVip Online Updating of Statistical Inference in the Big Data Setting ????Technometrics http://www.kidney.de/pget.php?&tw=1&pmid=28018007 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28018007 #FOAvip English Wikipedia <ref>{{Cite pmid|28018007}}</ref> Online Updating of Statistical Inference in the Big Data Setting #MMPMID28018007 Schifano ED; Wu J; Wang C; Yan J; Chen MH Technometrics 2016[]; 58 (3): 393-403 PMID28018007show ga We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting. äOnline Updating of Statistical Inference in the Big Data Setting (epmc).pdf DeepDyve Pubget Overpricing