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The bootstrap is a method of computational inference that simulates the creation of new data by resampling from a single data set. We propose a new job for the bootstrap: generating inputs from one historical trace using Threshold Bootstrap. In this regard, the most important quality of bootstrap samples is that they be functionally indistinguishable from independent samples of the same stochastic process.
We describe a quantitative measure of difference between two time series, and demonstrate the sensitivity of this measure for discriminating between two data generating processes. Utilizing this distance measure for the task of generating inputs, we show a way of tuning the bootstrap using a single observed trace.
This application of the threshold bootstrap will be a powerful tool for Monte Carlo simulation. Monte Carlo simulation analysis relies on built-in input generators. These generators make unrealistic assumptions about independence and marginal distributions. The alternative source of inputs, historical trace data, though realistic by definition, provides only a single input stream for simulation. One benefit of our method would be expanding the number of inputs achieving reality by driving system models with actual historical input series. Another benefit might be the automatic generation of lifelike scenarios for the field of finance.
We describe a quantitative measure of difference between two time series, and demonstrate the sensitivity of this measure for discriminating between two data generating processes. Utilizing this distance measure for the task of generating inputs, we show a way of tuning the bootstrap using a single observed trace.
This application of the threshold bootstrap will be a powerful tool for Monte Carlo simulation. Monte Carlo simulation analysis relies on built-in input generators. These generators make unrealistic assumptions about independence and marginal distributions. The alternative source of inputs, historical trace data, though realistic by definition, provides only a single input stream for simulation. One benefit of our method would be expanding the number of inputs achieving reality by driving system models with actual historical input series. Another benefit might be the automatic generation of lifelike scenarios for the field of finance.
목차
Abstract
1. 서 론
2. 관련 연구
3. 부트스트랩 표본 시계열의 검정
4. 결 론
참고문헌
부 록
참고문헌 (21)
참고문헌 신청
[학술지(정기간행물)] - / 1981 / Some Asymptotic Theory for the Bootstrap Annals of Statistics 9 : 1196 ~ 1217
[학술지(정기간행물)] - / 1986 / Convincing User’s of Model’s Validity is Challenging Aspect of Modeler’s Job 18 : 74 ~ 85
[학술지(정기간행물)] - / 1979 / Bootstrap Methods：Another Look at the Jackknife Annals of Statistics 7 : 1 ~ 26
[학술지(정기간행물)] - / 1993. / An Introduction to the Bootstrap
[학술지(정기간행물)] - / 1993. / Blockwise Bootstrap for Dependent Observation：Higher Order Approximations for Studentized Statistics
[학술지(정기간행물)] - / 1986 / Convincing User’s of Model’s Validity is Challenging Aspect of Modeler’s Job 18 : 74 ~ 85
[학술지(정기간행물)] - / 1979 / Bootstrap Methods：Another Look at the Jackknife Annals of Statistics 7 : 1 ~ 26
[학술지(정기간행물)] - / 1993. / An Introduction to the Bootstrap
[학술지(정기간행물)] - / 1993. / Blockwise Bootstrap for Dependent Observation：Higher Order Approximations for Studentized Statistics
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UCI(KEPA) : I410-ECN-0101-2009-325-015230629