The neural network real time event selection for the DIRAC ewperiment at CERN is presented.It comprises of two independent parts.One uses plastic scintillators and the other the vertical Scintillating Fibres,The globa...The neural network real time event selection for the DIRAC ewperiment at CERN is presented.It comprises of two independent parts.One uses plastic scintillators and the other the vertical Scintillating Fibres,The global event decision is taken in less than 250 ns.Signal events are selected with an efficiency os more than 0.99 with a background rate reduction of about2.展开更多
Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first pr...Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.展开更多
文摘The neural network real time event selection for the DIRAC ewperiment at CERN is presented.It comprises of two independent parts.One uses plastic scintillators and the other the vertical Scintillating Fibres,The global event decision is taken in less than 250 ns.Signal events are selected with an efficiency os more than 0.99 with a background rate reduction of about2.
文摘Recurrent events data with a terminal event （e.g., death） often arise in clinical and ob- servational studies. Variable selection is an important issue in all regression analysis. In this paper, the authors first propose the estimation methods to select the significant variables, and then prove the asymptotic behavior of the proposed estimator. Furthermore, the authors discuss the computing algorithm to assess the proposed estimator via the linear function approximation and generalized cross validation method for determination of the tuning parameters. Finally, the finite sample estimation for the asymptotical covariance matrix is also proposed.
