• Communications for Statistical Applications and Methods
• /
• 제13권3호
• /
• pp.745-754
• /
• 2006

It has been shown that LAD estimates are more efficient than LS estimates when the error distribution is double exponential in AR(1) model. In order to explore the performance of LAD estimates one can use bootstrap approaches. In this paper we consider the efficiencies of bootstrap methods when we apply LAD estimates with highly variable data. Monte Carlo simulation results are given for comparing generalized bootstrap, stationary bootstrap and threshold bootstrap methods.

• Communications for Statistical Applications and Methods
• /
• 제24권1호
• /
• pp.1-13
• /
• 2017

This manuscript summarized advances in bootstrap methods for long-range dependent time series data. The stationary linear long-memory process is briefly described, which is a target process for bootstrap methodologies on time-domain and frequency-domain in this review. We illustrate time-domain bootstrap under long-range dependence, moving or non-overlapping block bootstraps, and the autoregressive-sieve bootstrap. In particular, block bootstrap methodologies need an adjustment factor for the distribution estimation of the sample mean in contrast to applications to weak dependent time processes. However, the autoregressive-sieve bootstrap does not need any other modification for application to long-memory. The frequency domain bootstrap for Whittle estimation is provided using parametric spectral density estimates because there is no current nonparametric spectral density estimation method using a kernel function for the linear long-range dependent time process.

• 산업경영시스템학회지
• /
• 제30권2호
• /
• pp.37-43
• /
• 2007

ANOVA is widely used for measurement system analysis. It assumes that the measurement error is normally distributed, which may not be seen in certain industrial cases. In this study, the exact and bootstrap confidence intervals for precision-to-tolerance ratio (PTR) are obtained for the cases where the measurement errors are normally and non-normally distributed and the reproducibility variation can be ignored. Lognormal and gamma distributions are considered for non-normal measurement errors. It is assumed that the quality characteristics have the same distributions of the measurement errors. Three different bootstrap methods of SB (Standard Bootstrap), PB (Percentile Bootstrap), and BCPB (Biased-Corrected Percentile Bootstrap) are used to obtain bootstrap confidence intervals for PTR. Based on a coverage proportion of PTR, a comparative study of exact and bootstrap methods is performed. Simulation results show that, for non-normal measurement error cases, the bootstrap methods of SB and BCPB are superior to the exact one.

• 한국경영과학회지
• /
• 제38권2호
• /
• pp.65-73
• /
• 2013

In the simulation output analysis, bootstrap method is an applicable resampling technique to insufficient data which are not significant statistically. The moving block bootstrap, the stationary bootstrap, and the threshold bootstrap are typical bootstrap methods to be used for autocorrelated time series data. They are nonparametric methods for stationary time series data, which correctly describe the original data. In the simulation output analysis, however, we may not use them because of the non-stationarity in the data set caused by the trend such as increasing or decreasing. In these cases, we can get rid of the trend by differencing the data, which guarantees the stationarity. We can get the bootstrapped data from the differenced stationary data. Taking a reverse transform to the bootstrapped data, finally, we get the pseudo-samples for the original data. In this paper, we introduce the applicability of bootstrap methods to the time series data having trend, and then verify it through the statistical analyses.

• Journal of the Korean Statistical Society
• /
• 제30권1호
• /
• pp.1-20
• /
• 2001

We present how bootstrap methods can be used to conduct inference on the rates of Poisson distributions when only the grouped data are available. A theoretical justification for the validity of bootstrap is given with an illustration of proposed method using a data set obtained fro ma pathology laboratory test. Traditional asymptotic methods are compared with bootstrap methods in computing the estimated standard errors and achieved significance levels for one sample and two sample tests. Bootstrap methods are shown to possess a nice property that he small sample distribution of the relevant statistics can be readily obtained from the bootstrap copies.

• 한국환경과학회지
• /
• 제20권3호
• /
• pp.321-327
• /
• 2011

Bootstrap methods is the computer-based resampling method that estimates the standard errors and confidence intervals of summary statistics using the plug-in principle for assessing the accuracy or uncertainty of statistical estimates, and the BCa method among the Bootstrap methods is known much superior to other Bootstrap methods in respect of the standards of statistical validation. Therefore this study suggests the method of the representation and treatment of uncertainty in flood risk assessment and water resources planning from the construction and application of rainfall frequency analysis model considersing the uncertainty based on the nonparametric BCa method among the Bootstrap methods for the assessement of the estimation of probability rainfall and the effect of uncertainty considering the uncertainty of the parameter estimation of probability in the rainfall frequency analysis that is the most fundamental in flood risk assessement and water resources planning.

• 한국시뮬레이션학회논문지
• /
• 제19권3호
• /
• pp.119-125
• /
• 2010

시뮬레이션 출력 분석 방법인 이동 블록 부트스트랩이나 정상 부트스트랩, 그리고 임계값 부트스트랩은 자기상관성이 존재하는 데이터에 적용 가능한 표본 재추출 방법론들이다. 이러한 부트스트랩 방법들은 데이터의 정상성을 가정하여 적용해 왔다. 그러나 실제 자료 또는 시뮬레이션 출력에 계절성이나 추세를 동반하여 그 정상성을 보장할 수 없는 경우에는 부트스트랩을 시뮬레이션 출력 분석에 적용하지 못하였다. 시뮬레이션 출력 분석 기법 중 자기상관성을 가장 잘 묘사하는 방법은 임계값 부트스트랩 방법이다. 임계값 부트스트랩은 자료의 임계값을 기준으로 주기를 형성하여 재추출하는 방법으로써 계절성이 존재하는 데이터에 부트스트랩을 적용한다면 임계값 부트스트랩과 유사한 정확도를 얻을 수 있다. 본 논문에서는 계절성이 존재하는 시계열 자료에 대한 부트스트랩 적용 가능성을 제시 및 검증해보고자 한다.

• Communications for Statistical Applications and Methods
• /
• 제23권6호
• /
• pp.587-592
• /
• 2016

It is a long-standing issue to correctly determine the number of long-run relationships among time series processes. We revisit nonparametric test for cointegration rank and propose bootstrap refinements. Consistent with model-free nature of the tests, we make use of Cholesky factor bootstrap methods, which require weak conditions for data generating processes. Simulation studies show that the original Breitung's test have difficulty in obtaining the correct size due to dependence in cointegrated errors. Our proposed bootstrapped tests considerably mitigate size distortions and represent a complementary approach to other bootstrap refinements, including sieve methods.

• Communications for Statistical Applications and Methods
• /
• 제7권3호
• /
• pp.859-869
• /
• 2000

In this paper, various methods for finding confidence intervals for p of binomial parameter are reviewed. Also we introduce tow bootstrap confidence intervals for p. We compare the performance of bootstrap methods with other methods in terms of average coverage probability by Monte Carlo simulation. Advantages of these bootstrap methods are discussed.

• Communications for Statistical Applications and Methods
• /
• 제14권2호
• /
• pp.365-376
• /
• 2007

This research considers to propose the control charts using median for the location parameter. In order to decide the control limits, we apply several bootstrap methods through the approach obtaining the confidence interval except the standard bootstrap method. Then we illustrate our procedure using an example and compare the performance among the various bootstrap methods by obtaining the length between control limits through the simulation study. The standard bootstrap may be apt to yield shortest length while the bootstrap-t method, the longest one. Finally we comment briefly about some specific features as concluding remarks.