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10.1007/s00362-021-01238-z http://scihub22266oqcxt.onion/10.1007/s00362-021-01238-z 34092925!8169437!34092925     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Pap+(Berl) 2022 ; 63 (2): 343-365 Nephropedia Template TP {{tp|p=34092925|t=2022. Kronecker delta method for testing independence between two vectors in high-dimension |pdf=|usr=}}{{34092925}} gab.com Text Kronecker delta method for testing independence between two vectors in high-dimension JO: Stat Pap (Berl) http://www.kidney.de/pget.php?&tw=1&pmid=34092925 #FOAvip Conventional methods for testing independence between two Gaussian vectors require sample sizes greater than the number of variables in each vector. Therefore, adjustments are needed for the high-dimensional situation, where the sample size is smaller than the number of variables in at least one of the compared vectors. It is critical to emphasize that the methods available in the literature are unable to control the Type I error probability under the nominal level. This fact is evidenced through an intensive simulation study presented in this paper. To cover this lack, we introduce a valid randomized test based on the Kronecker delta covariance matrices estimator. As an empirical application, based on a sample of companies listed on the stock exchange of Brazil, we test the independence between returns of stocks of different sectors in the COVID-19 pandemic context. Twit Text FOAVip Kronecker delta method for testing independence between two vectors in high-dimension ????Stat Pap (Berl) http://www.kidney.de/pget.php?&tw=1&pmid=34092925 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34092925 #FOAvip English Wikipedia <ref>{{Cite pmid|34092925}}</ref> Kronecker delta method for testing independence between two vectors in high-dimension #MMPMID34092925 Silva IR; Zhuang Y; Junior JCADS Stat Pap (Berl) 2022[]; 63 (2): 343-365 PMID34092925show ga Conventional methods for testing independence between two Gaussian vectors require sample sizes greater than the number of variables in each vector. Therefore, adjustments are needed for the high-dimensional situation, where the sample size is smaller than the number of variables in at least one of the compared vectors. It is critical to emphasize that the methods available in the literature are unable to control the Type I error probability under the nominal level. This fact is evidenced through an intensive simulation study presented in this paper. To cover this lack, we introduce a valid randomized test based on the Kronecker delta covariance matrices estimator. As an empirical application, based on a sample of companies listed on the stock exchange of Brazil, we test the independence between returns of stocks of different sectors in the COVID-19 pandemic context. äKronecker delta method for testing independence between two vectors in(epmc).pdf DeepDyve Pubget Overpricing