Author: Livio Fenga
Title: Forecasting the CoViD19 Diffusion in Italy and the Related Occupancy of Intensive Care Units Document date: 2020_4_1
ID: 4ffbqpkk_49
Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 /2020 Unlike standard bootstrap schemes, in the MEB case the resample set Ω mimics the observed realization of the underlying stochastic process, in MEB a large number of subsets, say {ω 1 , . . . , ω N } becomes the elements belonging to Ω, each of them containing a large number of replicates {x 1 , . . . , x J }. Among the important feat.....
Document: is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 /2020 Unlike standard bootstrap schemes, in the MEB case the resample set Ω mimics the observed realization of the underlying stochastic process, in MEB a large number of subsets, say {ω 1 , . . . , ω N } becomes the elements belonging to Ω, each of them containing a large number of replicates {x 1 , . . . , x J }. Among the important features of the MEB scheme, it is worth mentioning the consistency of its bootstrap samples with the ergodic theorem (see, e.g., Birkhoff (1931) ) and with the probabilistic structure of the observed time series. In Figure 3 an example of the application of MEB for the variable 1 V t,1 is given.
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