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10.1007/s00477-021-02021-0 http://scihub22266oqcxt.onion/10.1007/s00477-021-02021-0 33897300!8053745!33897300     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stoch+Environ+Res+Risk+Assess 2021 ; 35 (12): 2659-2678 Nephropedia Template TP {{tp|p=33897300|t=2021. COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning |pdf=|usr=}}{{33897300}} gab.com Text COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning JO: Stoch Environ Res Risk Assess http://www.kidney.de/pget.php?&tw=1&pmid=33897300 #FOAvip A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March 8, 2020 until May 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00477-021-02021-0. Twit Text FOAVip COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning ????Stoch Environ Res Risk Assess http://www.kidney.de/pget.php?&tw=1&pmid=33897300 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33897300 #FOAvip English Wikipedia <ref>{{Cite pmid|33897300}}</ref> COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning #MMPMID33897300 Torres-Signes A; Frias MP; Ruiz-Medina MD Stoch Environ Res Risk Assess 2021[]; 35 (12): 2659-2678 PMID33897300show ga A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March 8, 2020 until May 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00477-021-02021-0. äCOVID-19 mortality analysis from soft-data multivariate curve regressi(epmc).pdf DeepDyve Pubget Overpricing