• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.8
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• pp.1033-1038
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• 2004

LOD scores related to marbling scores and permutation test have been applied for the purpose detecting quantitative trait loci (QTL) and we selected a considerable major locus BM4311. K-means clustering, for the major DNA marker mining of BM4311 microsatellite loci in Hanwoo chromosome 6, has been tried and five traits are divided by three cluster groups. Then, the three cluster groups are classified according to six DNA markers. Finally, bootstrap test method to calculate confidence intervals, using resampling method, has been adapted in order to find major DNA markers. It could be concluded that the major markers of BM4311 locus in Hanwoo chromosome 6 were DNA marker 100 and 95 bp.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.1-17
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• 2022

We propose a test of consistency for two differentially private histograms using parametric bootstrap. The test can be applied when the original raw histograms are not available but only the differentially private histograms and the privacy level α are available. We also extend the test for the case where the privacy levels are different for different histograms. The resident population data of Korea and U.S in year 2020 are used to demonstrate the efficacy of the proposed test procedure. The proposed test controls the type I error rate at the nominal level and has a high power, while a conventional test procedure fails. While the differential privacy framework formally controls the risk of privacy leakage, the utility of such framework is questionable. This work also suggests that the power of a carefully designed test may be a viable measure of utility.

• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.257-265
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• 1996

We consider nonstationary autoregressive autoregressive process with infinite variance of error. In the case of infinite cariance, the limiting distribution of the estimated coefficient is different from that under the finite cariance assumption. In this paper we show that the bootstrap method can be used to approximate the distribution of ordinary least squares estimator of the coefficient in the first order random walk process with infinite variance through some empirical studies and we suggest a test procedure based on bootstrap method for the unit root test.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.107-117
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• 2004

Change point testing problem is considered. Kernel density estimators are used for constructing proposed change point test statistics. The proposed method can be used to the hypothesis testing of not only parameter change but also distributional change. Bootstrap method is applied to get the sampling distribution of proposed test statistic. Small sample Monte Carlo Simulation were also conducted in order to show the performance of proposed method.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.41-47
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• 2003

한우 6번 염색체 유전자 지도에서 한우의 질을 높이기 위한 QTL(quantitative trait loci)분석을 실시하여 선별된 Loci 값을 Permutation Test를 이용하여 계산하였다. 한편, 경제적으로 주요한 한우의 특성부위(질적부위와 육량등)에 따른, 우수 경제형질 DNA marker를 K-평균 군집법을 실시 파악하였다. 이들 QTL과 K-평균법에 의해 한우의 염색체 6번, ILST035의 주요 경제 형질별 DNA marker들을 선별하여, Bootstrap BCa방법을 이용하여 각 DNA marker들의 신뢰구간을 구했다.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.41-47
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• 2004

DNA marker 95bp and 100bp are selected as major DNA markers of the BM4311 microsatellite locus in progeny test Hanwoo chromosome 6 linkage map. This document is tried to know whether DNA marker 95bp and 100bp are also major DNA markers in Hanwoo performance valuation in chromosome 6 linkage map. The bootstrap BCa method will be used to calculate confidence interval for DNA markers.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.129-140
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• 2009

In this paper, we consider simultaneous confidence intervals for the difference of proportions between two groups taken from multivariate binomial distributions in a nonparametric way. We briefly discuss the construction of simultaneous confidence intervals using the method of adjusting the p-values in multiple tests. The features of bootstrap simultaneous confidence intervals using non-pooled samples are presented. We also compute confidence intervals from the adjusted p-values of multiple tests in the Westfall (1985) style based on a pooled sample. The average coverage probabilities of the bootstrap simultaneous confidence intervals are compared with those of the Bonferroni simultaneous confidence intervals and the Sidak simultaneous confidence intervals. Finally, we give an example that shows how the proposed bootstrap simultaneous confidence intervals can be utilized through data analysis.

• East Asian Economic Review
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• v.23 no.4
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• pp.333-351
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• 2019

We use a data-mining bootstrap procedure to investigate the predictability test in the eight Asia-Pacific regional stock markets using in-sample and out-of-sample forecasting models. We address ourselves to the data-mining bias issues by using the data-mining bootstrap procedure proposed by Inoue and Kilian and applied to the US stock market data by Rapach and Wohar. The empirical findings show that stock returns are predictable not only in-sample but out-of-sample in Hong Kong, Malaysia, Singapore, and Korea with a few exceptions for some forecasting horizons. However, we find some significant disparity between in-sample and out-of-sample predictability in the Korean stock market. For Hong Kong, Malaysia, and Singapore, stock returns have predictable components both in-sample and out-of-sample. For the US, Australia, and Canada, we do not find any evidence of return predictability in-sample and out-of-sample with a few exceptions. For Japan, stock returns have a predictable component with price-earnings ratio as a forecasting variable for some out-of-sample forecasting horizons.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.151-165
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• 2006

It is important to estimate the true misclassification rate of a given classifier when an independent set of test data is not available. Cross-validation and bootstrap are two possible approaches in this case. In related literature bootstrap estimators of the true misclassification rate were asserted to have better performance for small samples than cross-validation estimators. We compare the two estimators empirically when the classification rule is so adaptive to training data that its apparent misclassification rate is close to zero. We confirm that bootstrap estimators have better performance for small samples because of small variance, and we have found a new fact that their bias tends to be significant even for moderate to large samples, in which case cross-validation estimators have better performance with less computation.