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10.1111/biom.12236 http://scihub22266oqcxt.onion/10.1111/biom.12236 C4383722!4383722 !25327216     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25327216 &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\25327216 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Biometrics 2015 ; 71 (1 ): 247-257 Nephropedia Template TP {{tp|p=25327216 |t=2015. Structured functional principal component analysis |pdf=|usr=}}{{25327216 }} gab.com Text Structured functional principal component analysis JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=25327216 #FOAvip Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for high-dimensional data. Methods are used in applications including high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep. Twit Text FOAVip Structured functional principal component analysis ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=25327216 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25327216 #FOAvip English Wikipedia <ref>{{Cite pmid|25327216 }}</ref> Structured functional principal component analysis #MMPMID25327216 Shou H ; Zipunnikov V ; Crainiceanu CM ; Greven S Biometrics 2015[Mar]; 71 (1 ): 247-257 PMID25327216 show ga Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for high-dimensional data. Methods are used in applications including high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep. |*Algorithms [MESH] |*Data Interpretation, Statistical [MESH] |*Principal Component Analysis [MESH] |Diagnosis, Computer-Assisted/*methods [MESH] |Humans [MESH] |Monitoring, Physiologic/*methods [MESH] |Numerical Analysis, Computer-Assisted [MESH] |Reproducibility of Results [MESH]Structured functional principal component analysis (epmc).pdf DeepDyve Pubget Overpricing