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10.1002/sta4.89 http://scihub22266oqcxt.onion/10.1002/sta4.89 C4652857!4652857 !26594358     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26594358 &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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26594358 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Stat+(Int+Stat+Inst) 2015 ; 4 (1 ): 212-226 Nephropedia Template TP {{tp|p=26594358 |t=2015. Longitudinal Functional Data Analysis |pdf=|usr=}}{{26594358 }} gab.com Text Longitudinal Functional Data Analysis JO: Stat (Int Stat Inst) http://www.kidney.de/pget.php?&tw=1&pmid=26594358 #FOAvip We consider dependent functional data that are correlated because of a longitudinal-based design: each subject is observed at repeated times and at each time a functional observation (curve) is recorded. We propose a novel parsimonious modeling framework for repeatedly observed functional observations that allows to extract low dimensional features. The proposed methodology accounts for the longitudinal design, is designed to study the dynamic behavior of the underlying process, allows prediction of full future trajectory, and is computationally fast. Theoretical properties of this framework are studied and numerical investigations confirm excellent behavior in finite samples. The proposed method is motivated by and applied to a diffusion tensor imaging study of multiple sclerosis. Twit Text FOAVip Longitudinal Functional Data Analysis ????Stat (Int Stat Inst) http://www.kidney.de/pget.php?&tw=1&pmid=26594358 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26594358 #FOAvip English Wikipedia <ref>{{Cite pmid|26594358 }}</ref> Longitudinal Functional Data Analysis #MMPMID26594358 Park SY ; Staicu AM Stat (Int Stat Inst) 2015[]; 4 (1 ): 212-226 PMID26594358 show ga We consider dependent functional data that are correlated because of a longitudinal-based design: each subject is observed at repeated times and at each time a functional observation (curve) is recorded. We propose a novel parsimonious modeling framework for repeatedly observed functional observations that allows to extract low dimensional features. The proposed methodology accounts for the longitudinal design, is designed to study the dynamic behavior of the underlying process, allows prediction of full future trajectory, and is computationally fast. Theoretical properties of this framework are studied and numerical investigations confirm excellent behavior in finite samples. The proposed method is motivated by and applied to a diffusion tensor imaging study of multiple sclerosis. äLongitudinal Functional Data Analysis (epmc).pdf DeepDyve Pubget Overpricing