Author: Livio Fenga
Title: CoViD--19: An Automatic, Semiparametric Estimation Method for the Population Infected in Italy Document date: 2020_3_18
ID: hhj7zte1_23
Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 /2020 proposed, many of which now freely and publicly available in the form of powerful routines working under software package such as Python R or R R . In more details, while in the classic bootstrap an ensemble Ω represents the population of reference the observed time series is drawn from, in MEB a large number of ensembles (subsets), say .....
Document: is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 /2020 proposed, many of which now freely and publicly available in the form of powerful routines working under software package such as Python R or R R . In more details, while in the classic bootstrap an ensemble Ω represents the population of reference the observed time series is drawn from, in MEB a large number of ensembles (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 }. Perhaps, the most important characteristic of the MEB algorithm is that its design guarantees the inference process to satisfy the ergodic theorem. Formally, denoting by the symbol | · | the cardinality function (counting function) of a given ensemble of time series {x t ∈ ω i ; i = 1, . . . , N }, the MEB procedure generates a set of disjoint subsets
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