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Wang Wei1; Ching Wai Ki2; Wang Shouyang1; Yu Lean3
Source Publicationjournalofsystemsscienceandinformation
AbstractMonte Carlo simulation is an efficient method to estimate quantile. However, it becomes a serious problem when a huge sample size is required but the memory is insufficient. In this paper, we apply the stream quantile algorithm to Monte Carlo simulation in order to estimate quantile with limited memory. A rigorous theoretical analysis on the properties of the ?_n-approximate quantile is proposed in this paper. We prove that if ?_n = o(n~(-1/2)),then the ?_n-approximate α-quantile computed by any deterministic stream quantile algorithm is a consistent and asymptotically normal estimator of the true quantile q_α. We suggest setting ?_n = 1/(n~(1/2) log_(10) n) in practice. Two deterministic stream quantile algorithms, including of GK algorithm and ZW algorithm, are employed to illustrate the performance of the ?_n-approximate quantile. The numerical example shows that the deterministic stream quantile algorithm can provide desired estimator of the true quantile with less memory.
Document Type期刊论文
Recommended Citation
GB/T 7714
Wang Wei,Ching Wai Ki,Wang Shouyang,et al. quantilesonstreamanapplicationtomontecarlosimulation[J]. journalofsystemsscienceandinformation,2016,4(4):334.
APA Wang Wei,Ching Wai Ki,Wang Shouyang,&Yu Lean.(2016).quantilesonstreamanapplicationtomontecarlosimulation.journalofsystemsscienceandinformation,4(4),334.
MLA Wang Wei,et al."quantilesonstreamanapplicationtomontecarlosimulation".journalofsystemsscienceandinformation 4.4(2016):334.
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