• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.339-347
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• 2006

In mixture experiments, performing experiments in highly constrained regions causes collinearity problems. We can use the ridge regression as a means for stabilizing the coefficient estimators in the fitted model. But there is no theory available on which to base statistical inference of ridge estimators. The bootstrap technique could be used to seek the confidence intervals for ridge estimators.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.39-43
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• 2000

분산 추정 및 신뢰구간 추정의 한 방법으로 널리 쓰이고 있는 붓스트랩 방법을 복합표본에 적용하는 방법에 대해 알아보았다. 복합 표본은 유한 모집단에서 추출되고 추출확률이 다르기 때문에 i.i.d. 표본에 기초하여 개발된 전통적인 붓스트랩 방법을 직접 적용하면 추론의 오류가 발생할 수 있다. 본 연구에서는 복원 확률비례표본과 랜덤그룹표본에 붓스트랩을 적용하는 방법을 알아보았다.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.405-414
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• 2012

In a stratified sample, the sampling frame is divided into non-overlapping groups or strata (e.g. geographical areas, age-groups, and genders). A sample is taken from each stratum, if this sample is a simple random sample it is referred to as stratified random sampling. In this paper, we study the bootstrap inference (including confidence interval) and test for a stratified population mean. We also introduce the bootstrap consistency based on limiting distribution related to the plug-in estimator of the population mean. We suggest three bootstrap confidence intervals such as standard bootstrap method, percentile bootstrap method and studentized bootstrap method. We also suggest a bootstrap test method computing the $ASL_{boot}$(Achieved Significance Level). The results of estimation are verified using simulation.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.99-109
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• 2019

We compared the confidence intervals of estimators using various bootstrap methods for a Random Coefficient Autoregressive(RCA) model. We consider a Quasi score estimator and M-Quasi score estimator using Huber, Tukey, Andrew and Hempel functions as bounded functions, that do not have required assumption of distribution. A standard bootstrap method, percentile bootstrap method, studentized bootstrap method and hybrid bootstrap method were proposed for the estimations, respectively. In a simulation study, we compared the asymptotic confidence intervals of the Quasi score and M-Quasi score estimator with the bootstrap confidence intervals using the four bootstrap methods when the underlying distribution of the error term of the RCA model follows the normal distribution, the contaminated normal distribution and the double exponential distribution, respectively.

• The Korean Journal of Applied Statistics
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• v.3 no.1
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• pp.121-141
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• 1990

Computer intensive bootstrap methods are studied as a tool of statistics. Practical calculation and theoretical justification problem of the methods in estimating the sampling distribution and construction confidence region of parameters are discussed through several examples. Statistical meaning of the methods are also considered.

• 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.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.221-226
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• 1995

본 연구는 붓스트랩에 의한 U-통계량의 분산추정방법을 제안하고, 추정량의 일치성을 증명하였다. 결과적으로 붓스트랩 추정량으로 표준화한 U-통계량의 값이 표준정규분포에 근사함을 보였다. 또한 실제적인 비모수검정에서 이를 응용하여 검정력과 특성을 연구하였다.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.151-161
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• 1998

We study the bootstrap interval estimation for survival function in the Koziol-Green model. We construct the approximate bootstrap confidence intervals for survival function and prove the strong consistency for the bootstrap estimator of survival function. Finally we show that the approximate bootstrap confidence intervals are better in terms of coverage probability than confidence intervals based on asymptotic normal distribution and transformations of survival function via Monte Carlo simulation study.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.341-350
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• 2012

Composite quantile regression model is considered for iid error case. Since the regression coefficients are the same across different quantiles, composite quantile regression can be used to combine the strength across multiple quantile regression models. For the composite quantile regression, bootstrap method is examined for statistical inference including the selection of the number of quantiles and confidence intervals for the regression coefficients. Feasibility of the bootstrap method is demonstrated through a simulation study.

• 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.