• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.449-462
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• 2016

This paper considers a long-run variance estimation using a block bootstrap method under strong dependence also known as long range dependence. We extend currently available methods in two ways. First, it extends bootstrap methods under short range dependence to long range dependence. Second, to accommodate the observation that strong dependence may come from deterministic trend plus noise models, we propose to utilize residuals obtained from the nonparametric kernel estimation with the bimodal kernel. The simulation study shows that our method works well; in addition, a data illustration is presented for practitioners.

• Journal of Multimedia Information System
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• v.2 no.3
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• pp.255-262
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• 2015

The modern manufacturing systems and technologies produce products that are more accurate day by day. This can be reached mainly by improvement the manufacturing process with at the same time restricting more and more the quality specifications and reducing the uncertainty in part. The main objective an industry becomes to lower the part's variability, since the less variability - the better is product. One of the part of this task is measuring the object's uncertainty. The main purpose of this study is to understand the application of bootstrap method for uncertainty evaluation. Bootstrap method is a collection of sample re-use techniques designed to estimate standard errors and confidence intervals. In the case study a surface of an automobile engine block - (Top view side) is measured by Coordinate Measuring Machine (CMM) and analyzed for uncertainty using Geometric Least Squares in complex with bootstrap method. The designed experiment is composed by three similar measurements (the same features in unique reference system), but with different points (5, 10, 20) concentration at each level. Then each cloud of points was independently analyzed by means of non-linear Least Squares, after estimated results have been reported. A MatLAB software tool used to generate new samples using bootstrap function. The results of the designed experiment are summarized and show that the bootstrap method provides the possibility to evaluate the uncertainty without repeating the Coordinate Measuring Machine (CMM) measurements many times, i.e. potentially can reduce the measuring time.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.27-37
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• 2010

This article concerns the forecasting in binomial AR(p) models which is proposed by Wei$\ss$ (2009b) for time series of binomial counts. Our method extends to binomial AR(p) models a recent result by Jung and Tremayne (2006) for integer-valued autoregressive model of second order, INAR(2), with simple Poisson innovations. Forecasts are produced by conditional median which gives 'coherent' forecasts, and we estimate the forecast distributions of future values of binomial AR(p) models by means of a Monte Carlo method allowing for parameter uncertainty. Model parameters are estimated by the method of moments and estimated standard errors are calculated by means of block of block bootstrap. The method is fitted to log data set used in Wei$\ss$ (2009b).

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.39-56
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• 2000

${\overline{X}}$ control chart has proven to be an effective tool to improve the product quality. Shewhart charts assume that the observations are independent and normally distributed. Under the presence of positive autocorrelation and severe skewness, the control limits are not accurate because assumptions are violated- Autocorrelation in process measurements results in frequent false alarms when standard control chats are applied in process monitoring. In this paper, Threshold Bootstrap and Moving Block Bootstrap are used for constructing a confidence interval of correlated observations. Monte Carlo simulation studies are conducted to compare the performance of the bootstrap methods and that of standard method for constructing control charts under several conditions.

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.407-418
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• 2001

Buhlmann(1944) established the validity of the block bootstrap proposed by Kunsch when it is applied to p-dimensional $\alpha$-mixing dependent sequence. But his result requires a rather restrictive condition on p in the sense that p is entangled with dependence structure. We address that such restriction on p(or complication of dependence structure with p) could be removed completely when the underlying dependence structure is replace by more weakly dependent structure such as ø-mixing.

• Journal of Korean Society for Quality Management
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• v.24 no.2
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• pp.77-86
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• 1996

In this paper, we consider the problem of testing independence in the absolutely continuous bivariate exponential distribution of Block and Basu(1974). We construct a bootstrap procedure for testing zero and non-zero values of the parameter ${\lambda}_3$ which measures the degree of dependence and compare the power of the bootstrap test with likelihood ratio test(LRT) by Gupta et al.(1984) and the test based on maximum likelihood estimator(MLE) $\hat{{\lambda}}_3$ by Hanagal and Kale(1991) for small and moderate sample sizes.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.611-622
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• 2019

This paper considers the estimation of the long memory parameter in nonparametric regression with strongly correlated errors. The key idea is to minimize a unified mean squared error of long memory parameter to select both kernel bandwidth and the number of frequencies used in exact local Whittle estimation. A unified mean squared error framework is more natural because it provides both goodness of fit and measure of strong dependence. The block bootstrap is applied to evaluate the mean squared error. Finite sample performance using Monte Carlo simulations shows the closest performance to the oracle. The proposed method outperforms existing methods especially when dependency and sample size increase. The proposed method is also illustreated to the volatility of exchange rate between Korean Won for US dollar.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.306-306
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• 2018

일반적으로 경향성 분석은 연최대강우량으로부터 산정하고 있으나 본 연구에서는 강우자료 이외의 자료를 이용하여 경향성 분석을 하고자 한다. 이를 위해 국내에서 가장 신뢰할 수 있는 기상청 산하의 강우량으로부터 연최대강우량을 추출하고 월단위인 월별 스케일 지수를 추정하였다. 비교를 위해 경향성 분석 방법 중 가장 널리 사용되고 있는 Mann-Kendall (MK) 분석을 사용하였고, 추가적으로 MK 분석의 단점을 보완할 수 있는 the block bootstrap-based MK (BBS-MK) 분석을 적용하였다. 연최대강우량을 이용한 경향성 분석 결과는 홍수기 기간에 해당하는 7월부터 10월까지의 경향성은 뚜렷하게 나타나지 않았다. 그러나 스케일 지수에 대한 경향성 분석 결과에서는 몇몇 지점에서 감소 또는 증가하는 경향을 보이는 강우 지점들을 확인할 수 있었다. 따라서 경향성 분석을 위해서는 연최대강우량뿐만이 아니라 다양한 인자를 이용한 경향성 분석이 필요함을 확인하였다.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.339-339
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• 2019

지구 온난화와 기후변화의 영향으로 태풍의 발생과 집중호우로 인한 홍수피해는 꾸준히 증가하고 있는 실정이다. 이와 같이 홍수와 관련된 강수량은 기상인자 중에서 유역의 유출과 관계가 있고, 유역의 내수침수 등의 도시홍수를 일으키는 원인이 되고 있다. 그러나 본 연구에서는 자료의 경향성을 판단하기 위해 국내 연최대강우자료의 경향성을 분석하였으며, 또한 연최대강우자료의 시간적 특성을 나타내는 지표로써 스케일 지수에 대하여 경향성과 변동성을 분석하였다. 이를 위해 the block bootstrap-based MK (BBS-MK) 분석을 실시하였고, 연최대강우자료로부터 BBS-MK분석에 대한 경향성 분석 결과, 다수 지점의 연최대강우자료에서 경향성이 나타나지 않았으나, 큰 변동성을 확인하였고, 연별 스케일 지수의 변동성 보다 월별 스케일 지수의 변동성 중 우기에 해당하는 월 (6-10월)에 대한 변동성은 크게 나타났다. 스케일 지수의 경향성은 연최대강우자료의 경향성 분석 결과와는 반대로 많은 강우지점에서 경향성이 존재함을 알 수 있었다. 스케일 지수의 경향성 분석 결과, 해안 지역의 강우 관측소에서 감소 경향이 확인되었지만, 내륙 지역에서는 스케일 지수가 증가하는 경향을 확인할 수 있었다.