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10.1002/sim.9026 http://scihub22266oqcxt.onion/10.1002/sim.9026 33973260!8242591!33973260     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 249.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Med 2021 ; 40 (19): 4252-4268 Nephropedia Template TP {{tp|p=33973260|t=2021. Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information |pdf=|usr=}}{{33973260}} gab.com Text Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=33973260 #FOAvip Since the outbreak of the new coronavirus disease (COVID-19), a large number of scientific studies and data analysis reports have been published in the International Journal of Medicine and Statistics. Taking the estimation of the incubation period as an example, we propose a low-cost method to integrate external research results and available internal data together. By using empirical likelihood method, we can effectively incorporate summarized information even if it may be derived from a misspecified model. Taking the possible uncertainty in summarized information into account, we augment a logarithm of the normal density in the log empirical likelihood. We show that the augmented log-empirical likelihood can produce enhanced estimates for the underlying parameters compared with the method without utilizing auxiliary information. Moreover, the Wilks' theorem is proved to be true. We illustrate our methodology by analyzing a COVID-19 incubation period data set retrieved from Zhejiang Province and summarized information from a similar study in Shenzhen, China. Twit Text FOAVip Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=33973260 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33973260 #FOAvip English Wikipedia <ref>{{Cite pmid|33973260}}</ref> Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information #MMPMID33973260 Jiang Z; Yang B; Qin J; Zhou Y Stat Med 2021[Aug]; 40 (19): 4252-4268 PMID33973260show ga Since the outbreak of the new coronavirus disease (COVID-19), a large number of scientific studies and data analysis reports have been published in the International Journal of Medicine and Statistics. Taking the estimation of the incubation period as an example, we propose a low-cost method to integrate external research results and available internal data together. By using empirical likelihood method, we can effectively incorporate summarized information even if it may be derived from a misspecified model. Taking the possible uncertainty in summarized information into account, we augment a logarithm of the normal density in the log empirical likelihood. We show that the augmented log-empirical likelihood can produce enhanced estimates for the underlying parameters compared with the method without utilizing auxiliary information. Moreover, the Wilks' theorem is proved to be true. We illustrate our methodology by analyzing a COVID-19 incubation period data set retrieved from Zhejiang Province and summarized information from a similar study in Shenzhen, China. |*COVID-19[MESH] |*Infectious Disease Incubation Period[MESH] |Humans[MESH] |Likelihood Functions[MESH] |Research Design[MESH] |SARS-CoV-2[MESH]Enhanced empirical likelihood estimation of incubation period of COVID(epmc).pdf DeepDyve Pubget Overpricing