• Title, Summary, Keyword: Bootstrap confidence interval

Search Result 80, Processing Time 0.046 seconds

Bootstrap Confidence Intervals of the Process Capability Index Based on the EDF Expected Loss (EDF 기대손실에 기초한 공정능력지수의 붓스트랩 신뢰구간)

  • 임태진;송현석
    • Journal of the Korean Society for Quality Management
    • /
    • v.31 no.4
    • /
    • pp.164-175
    • /
    • 2003
  • This paper investigates bootstrap confidence intervals of the process capability index(PCI) based on the expected loss derived from the empirical distribution function(EDF). The PCI based on the expected loss is too complex to derive its confidence interval analytically, so the bootstrap method is a good alternative. We propose three types of the bootstrap confidence interval; the standard bootstrap(SB), the percentile bootstrap(PB), and the acceleration biased­corrected percentile bootstrap(ABC). We also perform a comprehensive simulation study under various process distributions, in order to compare the accuracy of the coverage probability of the bootstrap confidence intervals. In most cases, the coverage probabilities of the bootstrap confidence intervals from the EDF PCI turned out to be more accurate than those from the PCI based on the normal distribution. It is expected that the bootstrap confidence intervals from the EDF PCI can be utilized in real processes where the true distribution family may not be known.

Interval Estimations for Reliablility in Stress-Strength Model by Bootstrap Method

  • Lee, In-Suk;Cho, Jang-Sik
    • Journal of the Korean Data and Information Science Society
    • /
    • v.6 no.1
    • /
    • pp.73-83
    • /
    • 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.

  • PDF

Analysis of BOD Mean Concentration and Confidence Interval using Bootstrap Technique (Bootstrap 기법을 이용한 BOD 평균 농도 및 신뢰구간 분석)

  • Kim, Kyung Sub
    • Journal of Korean Society on Water Environment
    • /
    • v.26 no.2
    • /
    • pp.297-302
    • /
    • 2010
  • It is very important to know mean and confidence interval of water-quality constituents such as BOD for water-quality control and management of rivers and reservoirs effectively. The mean and confidence interval of BOD at Anseong2 and Hwangguji3 sampling stations which are located at the border of local governments in Anseong Stream were estimated and analyzed in this paper using Bootstrap technique which is one of non-parametric statistics. The results of Bootstrap were compared with arithmetic mean, geometric mean, Biweight method mean as a point estimator and distribution mean came from the appropriate probability distribution of Log-normal. In Bootstrap technique 12 data set was randomly selected in each year and 1000 samples was produced to get parameter of population. Visual Basic for Applications (VBA) of Microsoft Excel was utilized in Bootstrap. It was revealed that the Bootstrap technique can be used to explain more rigorously and robustly the achievement or violation of BOD target concentration in Total Maximum Daily Load (TMDL).

On the Performance of Iterated Wild Bootstrap Interval Estimation of the Mean Response

  • Kim, Woo-Chul;Ko, Duk-Hyun
    • Journal of the Korean Statistical Society
    • /
    • v.24 no.2
    • /
    • pp.551-562
    • /
    • 1995
  • We consider the iterated bootstrap method in regression model with heterogeneous error variances. The iterated wild bootstrap confidence intervla of the mean response is considered. It is shown that the iterated wild bootstrap confidence interval has coverage error of order $n^{-1}$ wheresa percentile method interval has an error of order $n^{-1/2}$. The simulation results reveal that the iterated bootstrap method calibrates the coverage error of percentile method interval successfully even for the small sample size.

  • PDF

Confidence Interval for Capability Process Indices by the Resampling Method (재표집방법에 의한 공정관리지수의 신뢰구간)

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

  • PDF

Confidence interval forecast of exchange rate based on bootstrap method during economic crisis (경제위기시 환율신뢰구간 예측 알고리즘 개발)

  • Kim, Tae-Yoon;Kwon, O-Jin
    • Journal of the Korean Data and Information Science Society
    • /
    • v.22 no.5
    • /
    • pp.895-902
    • /
    • 2011
  • This paper is mainly concerned about providing confidence prediction interval for exchange rate during economic crisis. Our proposed method is to use block bootstrap method for prediction interval for next day. It is shown that block bootstrap method is particularly effective for interval prediction of exchange rate during economic crisis.

Bootstrap and Delete-d Jackknife Confidence Intervals for Parameters of an Exponential Distribution

  • Kang, Suk-Bok;Cho, Young-Suk
    • Journal of the Korean Data and Information Science Society
    • /
    • v.8 no.1
    • /
    • pp.59-70
    • /
    • 1997
  • We introduce several estimators of the location and the scale parameters of the two-parameter exponential distribution, and then compare these estimators by the mean square error (MSE). Using the parametric bootstrap estimators and the delete-d jackknife, we obtain the bootstrap and the delete-d jackknife confidence intervals for the location and the scale parameters and compare the bootstrap confidence intervals with the delete-d jackknife confidence intervals by length and coverage probability through Monte Carlo method.

  • PDF

Bootstrap Confidence Bounds for P(X>Y)

  • Lee, In Suk;Cho, Jang Sik
    • Journal of the Korean Society for Quality Management
    • /
    • v.23 no.4
    • /
    • pp.64-73
    • /
    • 1995
  • In this paper, the stress strength model is assumed for the populations of X and Y, where distributions of X and Y are independent normal with unknown parameters. We construct bootstrap confidence intervals for reliability, R=P(X>Y) and compare the accuracy of the proposed bootstrap confidence intervals and classical confidence interval through Monte Carlo simulation.

  • PDF

Empirical Bayes Interval Estimation by a Sample Reuse Method

  • Cho, Kil-Ho;Choi, Dal-Woo;Chae, Hyeon-Sook
    • Journal of the Korean Data and Information Science Society
    • /
    • v.8 no.1
    • /
    • pp.41-48
    • /
    • 1997
  • We construct the empirical Bayes(EB) confidence intervals that attain a specified level of EB coverage for the unknown scale parameter in the Weibull distribution with the known shape parameter under the type II censored data. Our general approach is to use an EB bootstrap samples introduced by Larid and Louis(1987). Also, we compare the coverage probability and the expected interval length for these bootstrap intervals with those of the naive intervals through Monte Carlo simulation.

  • PDF

A Note on Comparing Multistage Procedures for Fixed-Width Confidence Interval

  • Choi, Ki-Heon
    • Communications for Statistical Applications and Methods
    • /
    • v.15 no.5
    • /
    • pp.643-653
    • /
    • 2008
  • Application of the bootstrap to problems in multistage inference procedures are discussed in normal and other related models. After a general introduction to these procedures, here we explore in multistage fixed precision inference in models. We present numerical comparisons of these procedures based on bootstrap critical points for small and moderate sample sizes obtained via extensive sets of simulated experiments. It is expected that the procedure based on bootstrap leads to better results.