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10.1080/10485252.2016.1231806 http://scihub22266oqcxt.onion/10.1080/10485252.2016.1231806 C5611856!5611856 !28955155     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28955155 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Nonparametr+Stat 2016 ; 28 (4 ): 813-838 Nephropedia Template TP {{tp|p=28955155 |t=2016. Classical Testing in Functional Linear Models |pdf=|usr=}}{{28955155 }} gab.com Text Classical Testing in Functional Linear Models JO: J Nonparametr Stat http://www.kidney.de/pget.php?&tw=1&pmid=28955155 #FOAvip We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications. Twit Text FOAVip Classical Testing in Functional Linear Models ????J Nonparametr Stat http://www.kidney.de/pget.php?&tw=1&pmid=28955155 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28955155 #FOAvip English Wikipedia <ref>{{Cite pmid|28955155 }}</ref> Classical Testing in Functional Linear Models #MMPMID28955155 Kong D ; Staicu AM ; Maity A J Nonparametr Stat 2016[]; 28 (4 ): 813-838 PMID28955155 show ga We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications. äClassical Testing in Functional Linear Models (epmc).pdf DeepDyve Pubget Overpricing