• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.62-62
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• 1999

In this paper, we propose a new technique of estimating tail of steady-state queue length distribution; Pr(Q>q), fo ATM multiplexer. Pr(Q>q) is a fundamental measure of network congestion. Assessing Pr(Q>q) properly is crucial for design and control of ATM networks. Data traffic pattern of high-speed networks is highly correlated and bursty. Estimating Pr(Q>q) is very difficult because of correlation and burstiness. We estimate entropy(rate-function) using large deviation principles and threshold bootstrap. Simulation studies are conducted to compare the performance of an existing method and our new method.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.195-198
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• 2004

One Important problem in a cancer microarray study is to identify a set of genes from which a molecular prognostic indicator can be developed. In parallel with this problem is to validate the chosen set of genes. We develop in this note a K-fold cross validation procedure by combining a 'pre-validation' technique and a bootstrap resampling procedure in the Cox regression . The pre-validation technique predicts the microarray predictor of a case without having seen the true class level of the case. It was suggested by Tibshirani and Efron (2002) to avoid the possible over-fitting in the regression in which a microarray based predictor is employed. The bootstrap resampling procedure for the Cox regression was proposed by Sauerbrei and Schumacher (1992) as a means of overcoming the instability of a stepwise selection procedure. We apply this K-fold cross validation to the microarray data of 92 gastric cancers of which the experiment was conducted at Cancer Metastasis Research Center, Yonsei University. We also share some of our experience on the 'false positive' result due to the information leak.

• 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 Society for Quality Management Conference
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• 1998.11a
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• pp.309-315
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• 1998

Process Capability can be expressed with a process index which indicates the incapability of a process to meet its specifications. This index is regarded as a process capability index(PCI) or more precisely as a process incapability index(PII). It is obtained from a simple transformation of a PCI. Greenwich and Jahr-Schaffrath(1995) considered the PII $C_{pp}$ which could be obtained from the transformation to the PCI, $C_{pm}$, and they provided the asymptotic distribution for $C_{pp}$ which was useful unless the process characteristic was normally distributed. However, some statistical inferences based on the asymptotic distribution need a large sample size. There are some processes which process engineers could not help obtaining sufficiently a large sample size. Thus, we have derived its corresponding bootstrap asymptotic distribution since bootstrapping would be a helpful technique for the PII, $C_{pp}$ which was nonparametric or free from assumptions of the distribution of the characteristic X. Moreover, we have constructed six bootstrap confidence intervals used in reducing bias of estimations based on the bootstrap asymptotic distribution and simulated their performances for $C_{pp}$,

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.753-761
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• 2019

A test procedure based on a Kendall's τ statistic is proposed for the association of bivariate interval censored data. In particular, a leverage bootstrap technique is applied to replace unknown failure times and a classical adjustment method is applied for treating tied observations. The suggested method shows desirable results in simulation studies. An AIDS dataset is analyzed with the suggested method.

• Journal of Korean Academy of Nursing
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• v.29 no.5
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• pp.1030-1041
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• 1999

This study examined those problems noticed under the Asymptotically Generalized Least Squares estimator in evaluating a structural model of physical health. The problems were highly correlated parameter estimates and high standard errors of some parameter estimates. Separate analyses of the endogenous part of the model and of the metric of a latent factor revealed a highly skewed and kurtotic measurement indicator as the focal point of the manifested problems. Since the sample sizes are far below that needed to produce adequate AGLS estimates in the given modeling conditions, the adequacy of the Maximum Likelihood estimator is further examined with the robust statistics and the bootstrap method. These methods demonstrated that the ML methods were unbiased and statistical decisions based upon the ML standard errors remained almost the same. Suggestions are made for future studies adopting structural equation modeling technique in terms of selecting of a reference indicator and adopting those statistics corrected for nonormality.

• Journal of Korean Society for Quality Management
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• v.39 no.2
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• pp.234-243
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• 2011

Multivariate control charts are widely used to monitor the performance of a multivariate process over time to maintain control of the process. Although existing multivariate control charts provide control limits to monitor the process and detect any extraordinary events, it is a challenge to identify the causes of an out-of-control alarm when the number of process variables is large. Several fault identification methods have been developed to address this issue. However, these methods require a normality assumption of the process data. In the present study, we propose a bootstrapped-based $T^2$ decomposition technique that does not require any distributional assumption. A simulation study was conducted to examine the properties of the proposed fault identification method under various scenarios and compare it with the existing parametric $T^2$ decomposition method. The simulation results showed that the proposed method produced better results than the existing one, especially in nonnormal situations.

• 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 Veterinary Clinics
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• v.24 no.4
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• pp.509-513
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• 2007

Parametric and nonparametric coupled with bootstrap simulation technique were used to reevaluate previously defined reference intervals of serum chemistry parameters. A population-based study was performed in 100 clinically healthy dogs that were retrieved from the medical records of Kangwon National University Animal Hospital during 2005-2006. Data were from 52 males and 48 females(1 to 8 years old, 2.2-5.8 kg of body weight). Chemistry parameters examined were blood urea nitrogen(BUN)(mg/dl), cholesterol(mg/dl), calcium(mg/dl), aspartate aminotransferase(AST)(U/L), alanine aminotransferase(ALT)(U/L), alkaline phosphatase(ALP)(U/L), and total protein(g/dl), and were measured by Ektachem DT 60 analyzer(Johnson & Johnson). All but calcium were highly skewed distributions. Outliers were commonly identified particularly in enzyme parameters, ranging 5-9% of the samples and the remaining were only 1-2%. Regardless of distribution type of each analyte, nonparametric methods showed better estimates for use in clinical chemistry compare to parametric methods. The mean and reference intervals estimated by nonparametric bootstrap methods of BUN, cholesterol, calcium, AST, ALT, ALP, and total protein were 14.7(7.0-24.2), 227.3(120.7-480.8), 10.9(8.1-12.5), 25.4(11.8-66.6), 25.5(11.7-68.9), 87.7(31.1-240.8), and 6.8(5.6-8.2), respectively. This study indicates that bootstrap methods could be a useful statistical method to establish population-based reference intervals of serum chemistry parameters, as it is often the case that many laboratory values do not confirm to a normal distribution. In addition, the results emphasize on the confidence intervals of the analytical parameters showing distribution-related variations.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.503-512
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• 2006

In this paper, the Inverse Weibull distribution parameters have been estimated using a new estimation technique based on the non-parametric kernel density function that introduced as an alternative and reliable technique for estimation in life testing models. This technique will require bootstrapping from a set of sample observations for constructing the density functions of pivotal quantities and thus the confidence intervals for the distribution parameters. The performances of this technique have been studied comparing to the conditional inference on the basis of the mean lengths and the covering percentage of the confidence intervals, via Monte Carlo simulations. The simulation results indicated the robustness of the proposed method that yield reasonably accurate inferences even with fewer bootstrap replications and it is easy to be used than the conditional approach. Finally, a numerical example is given to illustrate the densities and the inferential methods developed in this paper.