• Korean Management Science Review
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• v.22 no.1
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• pp.15-26
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• 2005

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.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.283-287
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• 2010

Stochastic simulation of hydrologic data has been widely developed for several decades. However, despite the several advances made in literature still a number of limitations and problems remain. In the current study, some stochastic simulation approaches tackling some of the existing problems are discussed. The presented models are based on nonparametric techniques such as block bootstrapping, and K-nearest neighbor resampling (KNNR), and kernel density estimate (KDE). Three different types of the presented stochastic simulation models are (1) Pilot Gamma Kernel estimate with KNNR (a single site case) and (2) Enhanced Nonparametric Disaggregation with Genetic Algorithm (a disaggregation case). We applied these models to one of the most challenging and critical river basins in USA, the Colorado River. These models are embedded into the hydrological software package, Pros and cons of the models compared with existing models are presented through basic statistics and drought and storage-related statistics.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.559-564
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• 2003

Recently, particle filters have attracted attentions for nonlinear state estimation. They evaluate a posterior probability distribution of the state variable based on observations in simulation using so-called importance sampling. However, degeneracy phenomena in the importance weights deteriorate the filter performance. A new filter, Evolution Strategies Based Particle Filter, is proposed to circumvent this difficulty and to improve the performance. Numerical simulation results illustrate the applicability of the proposed idea.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.1
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• pp.1-6
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• 2010

The Analytic Hierarchy Process (AHP) has been applied widely in various decision making fields. One of advantages of the AHP is the consistency test. However, it has several problems such as the limit of its concept, the limit of 9 scales and stern criteria, contradictory pairwise comparison. In this paper, we propose new criteria for the consistency with more realistic and ideal conditions. To derive the criteria, we conduct the simulation and use the bootstrap method, which is one of resampling techniques in the simulation area.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.349-374
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• 2019

A class imbalance problem arises when one class outnumbers the other class by a large proportion in binary data. Studies such as transforming the learning data have been conducted to solve this imbalance problem. In this study, we compared resampling methods among methods to deal with an imbalance in the classification problem. We sought to find a way to more effectively detect the minority class in the data. Through simulation, a total of 20 methods of over-sampling, under-sampling, and combined method of over- and under-sampling were compared. The logistic regression, support vector machine, and random forest models, which are commonly used in classification problems, were used as classifiers. The simulation results showed that the random under sampling (RUS) method had the highest sensitivity with an accuracy over 0.5. The next most sensitive method was an over-sampling adaptive synthetic sampling approach. This revealed that the RUS method was suitable for finding minority class values. The results of applying to some real data sets were similar to those of the simulation.

• Korean Journal of Organic Agriculture
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• v.21 no.4
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• pp.487-500
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• 2013

The purpose of this study is to estimate efficiency of environmental-friendly agricultural product by using Data Envelopment Analysis. A proposed method employs a bootstrapping approach to generating efficiency estimates through Monte Carlo simulation resampling process. The technical efficiency, pure technical efficiency, and scale efficiency measure of strawberry by pesticide-free certification is 0.967, 0.995, 0.968 respectively. However those of bias-corrected estimates are 0.918, 0.983, 0.934. We know that the DEA estimator is an upward biased estimator. In technical efficiency, average lower and upper confidence bounds of 0.807 and 0.960. According to these results, the DEA bootstrapping model used here provides bias-corrected and confidence intervals for the point estimates, it is more preferable.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.53-68
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• 1999

This paper presents a nonparametric method to test if the mean difference of DEA efficiency estimates between two groups statistically exists. A proposed method employs a bootstrapping approach to generation BCC efficiency estimates through Monte Carlo simulation resampling process. For the purpose of demonstration, we empirically apply the proposed method to the korean bank industry and compare its result with the result by the traditional deterministic DEA method. The nonparametric statistical hypothesis testing procedure in this study, which considers not only stochastic variability of the DEA data, but also random radial deviations off the efficient frontier, serves as a useful tool for dbjectively evaluating whether the mean difference of DEA efficiency estimates between groups is statistically significant.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.329-339
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• 2018

In this study, we propose tests for equality of several variances with the normality assumption. First of all, we propose the likelihood ratio test by applying the permutation principle. Then by using the p-values for the pairwise tests between variances and combination functions, we propose combination tests. We apply the permutation principle to obtain the overall p-values. Also we review the well- known test statistics for the completion of our discussion and modify a statistic with the p-values. Then we illustrate proposed tests by numerical and simulated data and compare their efficiency with the reviewed ones through a simulation study by obtaining empirical p-values. Finally, we discuss some interesting features related to the resampling methods and tests for equality among several variances.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.399-404
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• 2004

Recently GPS has been playing an increasingly important role in geodesy and positioning, for example, car navigation system, surveying, ITS(intelligent transport systems), LBS(Location Based Service) and so on. For telematics application, reception conditions of GPS signal are important. In some situation, such as in areas between buildings, metropolitan areas or areas with large skyscraper complexes, there are situations whereby the satellite signal is seriously restricted by various obstacles. Before the signal arrives at the receiver, it may be blocked, reflected, delayed, attenuated or scattered by terrestrial obstacles such as buildings. In this paper, we present satellite imagery data for telematics application. Therefore, for propriety of this studies, we made a GPS satellite visibility experiments in Bun-Dang on same time. This paper describes an approach to calculate building level using 0.6m, 1m, 6.6m resampling aerial polo imagery in stead of the satellite imagery and make a comparative study of accuracy. This paper tests the simulation of GPS signal using the building level.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.347-354
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• 1999

Statistical Process Control(SPC) which uses control charts is widely used to inspect and improve manufacturing process as a effective method. A parametric method is the most common in statistical process control. Shewhart chart was made under the assumption that measurements are independent and normal distribution. In practice, this assumption is often excluded, for example, in case of (equation omitted) chart, when the subgroup sample is small or correlation, it happens that measured data have bias or rejection of the normality test. A bootstrap method can be used in such a situation, which is calculated by resampling procedure without pre-distribution assumption. In this study, applying bootstrap percentile method to (equation omitted) chart, it is compared and evaluated standard process control limit with bootstrap percentile control limit. Also, under the normal and non-normal distributions, where parameter is 0.5, using computer simulation, it is compared standard parametric with bootstrap method which is used to decide process control limits in process quality.