• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.189-200
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• 2013

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation whether the training samples include missing values or not. We consider the conditional bootstrap confidence intervals for classification error rate when a block of observation is missing.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.75-81
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• 2004

We selected, in previous research, a major DNA Marker 235bp of ILSTS035 microsatellite locus in progeny test Hanwoo chromosome 6. We apply a major DNA Marker 235bp to perormance valuation Hanwoo chomosome 6. We use bootstrap BCa method and calculate confidence interval. A major DNA Marker 235bp is verified that it does not have environmental effect but affects primely economic trait factor.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.343-358
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• 2006

The distributional properties of forecasts in an integer-valued time series model have not been discovered yet mainly because of the complexity arising from the binomial thinning operator. We propose two bootstrap methods to obtain nonparametric prediction intervals for an integer-valued autoregressive model : one accommodates the variation of estimating parameters and the other does not. Contrary to the results of the continuous ARMA model, we show that the latter is better than the former in forecasting the future values of the integer-valued autoregressive model.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.365-376
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• 2007

This research considers to propose the control charts using median for the location parameter. In order to decide the control limits, we apply several bootstrap methods through the approach obtaining the confidence interval except the standard bootstrap method. Then we illustrate our procedure using an example and compare the performance among the various bootstrap methods by obtaining the length between control limits through the simulation study. The standard bootstrap may be apt to yield shortest length while the bootstrap-t method, the longest one. Finally we comment briefly about some specific features as concluding remarks.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.392-396
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• 2011

The purpose of this study was to develop the basic wood density (Abbreviated BWD) for Pinus densiflora and to evaluate the applicability of bootstrap simulation method. The data sets were divided into two groups based on eco-types in Korea, one from Gangwon type and the other from Jungbu type. The estimated BWDs derived from bootstrap simulation, which is one of the non-parametric statistics, were 0.418 ($g/cm^3$) in the Pinus densiflora in Gangwon while 0.464 ($g/cm^3$) in the Pinus densiflora in Jungbu. To evaluate the bootstrap simulation, the mean BWD, standard error and 95% confidence interval of probability density were estimated. The number of replication were 100, 500, 1,000, and 5,000 times that showed constant 95% confidence interval, while tended to decrease in terms of standard errors. The results of this study could be very useful to apply basic wood density values to calculate reliable carbon stocks for Pinus densiflora in Korea.

• Proceedings of the Korean Reliability Society Conference
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• 2002.06a
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• pp.185-194
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• 2002

No Abstract, See Full Text

• Journal of Korea Water Resources Association
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• v.36 no.3 s.134
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• pp.399-411
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• 2003

An approach determining a confidence interval of Nash's observed mean instantaneous unit hydrograph is developed. In the approach, both two parameters are treated as correlated gaussian random variables based on the theory of Box-Cox transformation and the regional similarity relation, so that linear statistical parameter estimation is possible. A parametric bootstrap method is adopted to give the confidence interval of the mean observed hydrograph. The proposed methodology is also applicable to estimate the parameters of Nash's model for un-gauged basins. An application to a watershed has shown that the proposed approach is adequate to assess the uncertainty of the Nash's hydrograph and to evaluate parameters for un-gauged basins.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.41 no.2
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• pp.107-112
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• 2008

To evaluate uncertainty and risk in biological reference points, we applied a bootstrapping method and a Bayesian procedure to estimate the related confidence intervals. Here we provide an example of the maximum sustainable yield (MSY) of turban shell, Batillus cornutus, estimated by the Schaefer and Fox models. Fitting the time series of catch and effort from 1968 to 2006 showed that the Fox model performs better than the Schaefer model. The estimated MSY and its bootstrap percentile confidence interval (CI) at ${\alpha}=0.05$ were 1,680 (1,420-1,950) tons for the Fox model and 2,170 (1,860-2,500) tons for the Schaefer model. The CIs estimated by the Bayesian approach gave similar ranges: 1,710 (1,450-2,000) tons for the Fox model and 2,230 (1,760-2,930) tons for the Schaefer model. Because uncertainty in effort and catch data is believed to be greater for earlier years, we evaluated the influence of sequentially excluding old data points by varying the first year of the time series from 1968 to 1992 to run 'backward' bootstrap resampling. The results showed that the means and upper 2.5% confidence limit (CL) of MSY varied greatly depending on the first year chosen whereas the lower 2.5% CL was robust against the arbitrary selection of data, especially for the Schaefer model. We demonstrated that the bootstrap and Bayesian approach could be useful in precautionary fisheries management, and we advise that the lower 2.5% CL derived by the Fox model is robust and a better biological reference point for the turban shells of Jeju Island.

• The Korean Journal of Physiology and Pharmacology
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• v.13 no.5
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• pp.367-371
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• 2009

The estimation of 90% parametric confidence intervals (CIs) of mean AUC and Cmax ratios in bioequivalence (BE) tests are based upon the assumption that formulation effects in log-transformed data are normally distributed. To compare the parametric CIs with those obtained from nonparametric methods we performed repeated estimation of bootstrap-resampled datasets. The AUC and Cmax values from 3 archived datasets were used. BE tests on 1,000 resampled data sets from each archived dataset were performed using SAS (Enterprise Guide Ver.3). Bootstrap nonparametric 90% CIs of formulation effects were then compared with the parametric 90% CIs of the original datasets. The 90% CIs of formulation effects estimated from the 3 archived datasets were slightly different from nonparametric 90% CIs obtained from BE tests on resampled datasets. Histograms and density curves of formulation effects obtained from resampled datasets were similar to those of normal distribution. However, in 2 of 3 resampled log (AUC) datasets, the estimates of formulation effects did not follow the Gaussian distribution. Bias-corrected and accelerated (BCa) CIs, one of the nonparametric CIs of formulation effects, shifted outside the parametric 90% CIs of the archived datasets in these 2 non-normally distributed resampled log (AUC) datasets. Currently, the 80~125% rule based upon the parametric 90% CIs is widely accepted under the assumption of normally distributed formulation effects in log-transformed data. However, nonparametric CIs may be a better choice when data do not follow this assumption.

• Proceedings of the Korean Association for Survey Research Conference
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• 2006.06a
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• pp.131-159
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• 2006

This paper consider multinomial group testing which is concerned with classification each of N given units into one of k disjoint categories. In this paper, we propose exact Bayesian, approximate Bayesian, bootstrap methods for estimating individual category proportions using the multinomial group testing model proposed by Bar-Lev et al (2005). By the comparison of Mcan Squre Error (MSE), it is shown that the exact Bayesian method has a bettor efficiency and consistency than maximum likelihood method. We suggest an approximate Bayesian approach using Markov Chain Monte Carlo (MCMC) for posterior computation. We derive exact credible intervals based on the exact Bayesian estimators and present confidence intervals using the bootstrap and MCMC. These intervals arc shown to often have better coverage properties and similar mean lengths to maximum likelihood method already available. Furthermore the proposed models are illustrated using data from a HIV blooding test study throughout California, 2000.