• 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.

• The Journal of Fisheries Business Administration
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• v.52 no.1
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• pp.23-46
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• 2021

The purpose of this study is to understand the production efficiency of individual fishing communities and provide directions for improvement. The subject of the study is aquaculture type Ochon-Gye in Goheung-gun. The analysis method used bootstrap-DEA to overcome the statistical reliability problem of the traditional DEA analysis technique. In addition, data mining-GIS was applied to identify the spatial productivity of fishing communities. The values of technology efficiency, pure technology efficiency, and scale efficiency were estimated for 32 aquaculture-type fishing villages. Then, using the benchmarking reference set and weights, the projection was presented through adjustment of the input factor excess, and furthermore, the confidence interval of the efficiency values considering statistical significance was estimated using bootstrap.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.269-285
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• 2016

In this paper, a simple step-stress accelerated life test (ALT) under progressive type-II censoring is considered. Progressive type-II censoring and accelerated life testing are provided to decrease the lifetime of testing and lower test expenses. The cumulative exposure model is assumed when the lifetime of test units follows an extension of the exponential distribution. Maximum likelihood estimates (MLEs) and Bayes estimates (BEs) of the model parameters are also obtained. In addition, a real dataset is analyzed to illustrate the proposed procedures. Approximate, bootstrap and credible confidence intervals (CIs) of the estimators are then derived. Finally, the accuracy of the MLEs and BEs for the model parameters is investigated through simulation studies.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.29-41
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• 2017

The estimation problem of expected time to failure of units is studied in a discrete set up. A simple step-stress accelerated life testing is considered with a Type-I censored sample from geometric distribution that is a commonly used distribution to model the lifetime of a device in discrete case. Maximum likelihood estimators as well as the associated distributions are derived. Exact, approximate and bootstrap approaches construct confidence intervals that are compared via a simulation study. Optimal confidence intervals are suggested in view of the expected width and coverage probability criteria. An illustrative example is also presented to explain the results of the paper. Finally, some conclusions are stated.

• Journal of the military operations research society of Korea
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• v.12 no.2
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• pp.68-81
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• 1986

This thesis studies the estimation from interarrival and service time data of the mean number of customers in service at time t for an $M/G/{\infty}$ queue. The assumption is that the parametric form of the service time distribution is unknown and the empirical distribution of twe service time is used in the estimate the mean number of customers in service. In the case in which the customer arrival rate is known the distribution of the estimate is derived and an approximate normal confidence interval procedure is suggested. The use of the nonparametric methods, which are the jackknife and the bootstrap, to estimate variability and construct confidence intervals for the estimate is also studied both analytically and by simulation.

• Food Science and Biotechnology
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• v.17 no.6
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• pp.1352-1356
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• 2008

Bootstrap method, a computer-intensive statistical technique to estimate the distribution of a statistic was applied to deal with uncertainty and variability of the experimental data in stochastic prediction modeling of microbial growth on a chill-stored food. Three different bootstrapping methods for the curve-fitting to the microbial count data were compared in determining the parameters of Baranyi and Roberts growth model: nonlinear regression to static version function with resampling residuals onto all the experimental microbial count data; static version regression onto mean counts at sampling times; dynamic version fitting of differential equations onto the bootstrapped mean counts. All the methods outputted almost same mean values of the parameters with difference in their distribution. Parameter search according to the dynamic form of differential equations resulted in the largest distribution of the model parameters but produced the confidence interval of the predicted microbial count close to those of nonlinear regression of static equation.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.4
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• pp.191-199
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• 2013

It is estimated and analyzed that the design wave height and the confidence interval (hereafter CI) according to the return period using the fourteen-year wave data obtained at Pusan New Port. The functions used in the extreme value analysis are the Gumbel function, the Weibull function, and the Kernel function. The CI of the estimated wave heights was predicted using one of the Monte-Carlo simulation methods, the Bootstrap method. The analysis results of the estimated CI of the design wave height indicate that over 150 years of data is necessary in order to satisfy an approximately ${\pm}$10% CI. Also, estimating the number of practically possible data to be around 25~50, the allowable error was found to be approximately ${\pm}$16~22% for Type I PDF and ${\pm}$18~24% for Type III PDF. Whereas, the Kernel distribution method, a typical non-parametric method, shows that the CI of the method is below 40% in comparison with the CI of the other methods and the estimated design wave height is 1.2~1.6 m lower than that of the other methods.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.461-473
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• 2014

We consider condence intervals for high quantiles of heavy-tailed distribution. The asymptotic condence intervals based on the limiting distribution of estimators are considered together with bootstrap condence intervals. We can also apply a non-parametric, parametric and semi-parametric approach to each of these two kinds of condence intervals. We considered 11 condence intervals and compared their performance in actual coverage probability and the length of condence intervals. Simulation study shows that two condence intervals (the semi-parametric asymptotic condence interval and the semi-parametric bootstrap condence interval using pivotal quantity) are relatively more stable under the criterion of actual coverage probability.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.41-47
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• 2004

DNA marker 95bp and 100bp are selected as major DNA markers of the BM4311 microsatellite locus in progeny test Hanwoo chromosome 6 linkage map. This document is tried to know whether DNA marker 95bp and 100bp are also major DNA markers in Hanwoo performance valuation in chromosome 6 linkage map. The bootstrap BCa method will be used to calculate confidence interval for DNA markers.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.485-494
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• 2015

Canonical correspondence analysis is an ordination method used to visualize the relationships among sites, species and environmental variables. However, projection results are fluctuations if the samples slightly change and consistent interpretation on ecological similarity among species tends to be difficult. We use the bootstrap methods for canonical correspondence analysis to solve this problem. The bootstrap method results show that the variations of coordinate points are inversely proportional to the number of observations and coverage rates with bootstrap confidence interval approximates to nominal probabilities.