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10.1080/01621459.2014.957287 http://scihub22266oqcxt.onion/10.1080/01621459.2014.957287 C4274811!4274811 !25544787     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\25544787 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Am+Stat+Assoc 2014 ; 109 (508 ): 1672-1682 Nephropedia Template TP {{tp|p=25544787 |t=2014. Generalized Ordinary Differential Equation Models |pdf=|usr=}}{{25544787 }} gab.com Text Generalized Ordinary Differential Equation Models JO: J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=25544787 #FOAvip Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The simulation study and application of our methods to an influenza viral dynamics study suggest that the proposed methods have a superior performance in terms of accuracy over the existing ODE model estimation approach and the extended smoothing-based (ESB) method. Twit Text FOAVip Generalized Ordinary Differential Equation Models ????J Am Stat Assoc http://www.kidney.de/pget.php?&tw=1&pmid=25544787 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25544787 #FOAvip English Wikipedia <ref>{{Cite pmid|25544787 }}</ref> Generalized Ordinary Differential Equation Models #MMPMID25544787 Miao H ; Wu H ; Xue H J Am Stat Assoc 2014[Oct]; 109 (508 ): 1672-1682 PMID25544787 show ga Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The simulation study and application of our methods to an influenza viral dynamics study suggest that the proposed methods have a superior performance in terms of accuracy over the existing ODE model estimation approach and the extended smoothing-based (ESB) method. äGeneralized Ordinary Differential Equation Models (epmc).pdf DeepDyve Pubget Overpricing