• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.73-83
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• 1995

We construct the approximate bootstrap confidence intervals for reliability (R) when the distributions of strength and stress are both normal. Also we propose percentile, bias correct (BC), bias correct acceleration (BCa), and percentile-t intervals for R. We compare with the accuracy of the proposed bootstrap confidence intervals and classical confidence interval based on asymptotic normal distribution through Monte Carlo simulation. Results indicate that the confidence intervals by bootstrap method work better than classical confidence interval. In particular, confidence intervals by BC and BCa method work well for small sample and/or large value of true reliability.

• Journal of Applied Reliability
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• v.1 no.1
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• pp.55-63
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• 2001

In this paper, we utilize the asymptotic variance of $C_{pk}$ to propose a two-sided confidence interval based on percentile-t bootstrap method. This confidence interval is compared with the ones based on the standard and percentile bootstrap methods. Simulation results show that percentile-t bootstrap method is preferred to other methods for constructing the confidence interval.l.

• 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.

• Communications for Statistical Applications and Methods
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• v.20 no.1
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• pp.41-52
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• 2013

The stationary bootstrap of Politis and Romano (1994) is adopted to develop prediction intervals of returns and volatilities in a generalized autoregressive heteroskedastic (GARCH)(p, q) model. The stationary bootstrap method is applied to generate bootstrap observations of squared returns and residuals, through an ARMA representation of the GARCH model. The stationary bootstrap estimators of unknown parameters are defined and used to calculate the stationary bootstrap samples of volatilities. Estimates of future values of returns and volatilities in the GARCH process and the bootstrap prediction intervals are constructed based on the stationary bootstrap; in addition, asymptotic validities are also shown.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.183-195
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• 1995

For a distribution on the unit sphere, the set of eigenvectors of the second moment matrix is a conventional measure of orientation. Asymptotic confidence cones for eigenvector under the parametric assumptions for the underlying distributions and nonparametric confidence cones for eigenvector based on bootstrapping were proposed. In this paper, to reduce the level error of confidence cones for eigenvector, double bootstrap confidence cones based on prepivoting are considered, and the consistency of this method is discussed. We compare the perfomances of double bootstrap method with the others by Monte Carlo simulations.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.521-529
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• 2005

In this paper, we consider a bootstrap method to the 'row and column effects model' (RC model) to analyze a contingency table with ordered variables. We propose a bootstrap procedure for testing of independence, equality of intervals, and goodness of fit in the RC model. A real data example is included.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.447-454
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• 2004

In this paper, we consider the bootstrap method to the interval estimation of the difference of quantiles of right censored data. We showed the validity of bootstrap method and compare with others with real data example. In simulation various resampling schemes for right censored data are also considered.

• 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.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.375-382
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• 2010

In this study, we propose a robust estimator of the multivariate location parameter by means of the spatial median based on data trimming which extending trimmed mean in the univariate setup. The trimming quantity of this estimator is determined by the bootstrap method, and its covariance matrix is estimated by using the double bootstrap method. This extends the work of Jhun et al. (1993) to the multivariate case. Monte Carlo study shows that the proposed trimmed spatial median estimator yields better efficiency than a spatial median, while its covariance matrix based on double bootstrap overcomes the under-estimating problem occurred on single bootstrap method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.4
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• pp.1-7
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• 2002

Statistical process control is used widely as an effective tool to solve the quality problems in practice fields. All the control charts used in statistical process control are parametric methods, suppose that the process distributes normal and observations are independent. But these assumptions, practically, are often violated if the test of normality of the observations is rejected and/or the serial correlation is existed within observed data. Thus, in this study, to screening process, the Combined Shewhart - CUSUM quality control chart is described and evaluated that used bootstrap method. In this scheme the CUSUM chart will quickly detect small shifts form the goal while the addition of Shewhart limits increases the speed of detecting large shifts. Therefor, the CSC control chart is detected both small and large shifts in process, and the simulation results for its performance are exhibited. The bootstrap CSC control chart proposed in this paper is superior to the standard method for both normal and skewed distribution, and brings in terms of ARL to the same result.