Kedem B, Pyne S. Estimation of Tail Probabilities by Repeated Augmented Reality.
JOURNAL OF STATISTICAL THEORY AND PRACTICE 2021;
15:25. [PMID:
33495693 PMCID:
PMC7816841 DOI:
10.1007/s42519-020-00152-1]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/22/2020] [Indexed: 11/27/2022]
Abstract
Synthetic data, when properly used, can enhance patterns in real data and thus provide insights into different problems. Here, the estimation of tail probabilities of rare events from a moderately large number of observations is considered. The problem is approached by a large number of augmentations or fusions of the real data with computer-generated synthetic samples. The tail probability of interest is approximated by subsequences created by a novel iterative process. The estimates are found to be quite precise.
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