• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1401-1408
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• 2013

Kim and Kim (1992) proposed typical nonparametric method for umbrella alternative in randomized block design with replications. In this paper, We consider a test procedure for umbrella alternatives in a randomized block design using extension of the two sample placement tests described in Orban and Wolfe (1982) and treatment tests described in Kim (1999). We perform a Monte Carlo study to compare the empirical powers of the test statistics for underlying distributions.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1105-1112
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• 2011

Mack (1981), Skilling and Wolfe (1977, 1978) proposed typical nonparametric method in randomized block design with replications. In this paper, we proposed the procedures based on placement as extension of the two sample placement tests described in Orban and Wolfe (1982) and treatment versus control tests described in Kim (1999). Also Monte Carlo simulation study is adapted to compare power of the proposed procedure with those of previous procedures.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1433-1446
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• 2013

In this study, we proposed a methodology to develop Rating Curves for high water level using rainfall generation by the Monte Carlo Simulation (MCS) technique, optimized rainfall-runoff model, and flood routing model in an urban stream. The developed stage discharge Rating Curve based on observed data was contained flow measurement errors and uncertainties. The standard error ($S_e$) for observations was 0.056, and the random uncertainty ($2S_{mr}$) was analyzed by ${\pm}1.43%$ on average, and up to ${\pm}4.27%$. Moreover, it was found that the Rating Curve extensions by way of logarithmic and Stevens methods were overestimated to compare with the urban basin scale. Finally, we confirmed that the high water level extension by random generation of hydrological data using MCS can be reduced uncertainty of the high water level, and it will consider as a more reliable approach for high water level extension. In the near future, this results can be applied to real-time flood alert system for urban streams through construction of the high water level extension system using MCS procedures.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.179-188
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• 1998

Local polynomial regression estimation is the most popular one among kernel type regression estimator. In local polynomial regression function esimation bandwidth selection is crucial problem like the kernel estimation. When the regression curve has complicated structure variable bandwidth selection will be appropriate. In this paper, we propose a variable bandwidth selection method fully data driven. We will choose the bandwdith by selecting minimising estiamted MSE which is estimated by the pilot bandwidth study via croos-validation method. Monte carlo simulation was conducted in order to show the superiority of proposed bandwidth selection method.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.97-111
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• 1998

This paper suggests a Bayesian method for testing first-order markov correlation among linear regression disturbances. As a Bayesian test criterion, Bayes factor is derived in the form of generalized Savage-Dickey density ratio that is easily estimated by means of posterior simulation via Gibbs sampling scheme. Performance of the Bayesian test is evaluated and examined based upon a Monte Carlo experiment and an empirical data analysis. Efficiency of the posterior simulation is also examined.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.1-18
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• 2000

In this paper, we study several parameter estimation methods used for autoregressive processes and compare them in view of forecasting. The least square estimation, least absolute deviation estimation, robust estimation are compared through Monte Carlo simulations.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1027-1036
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• 2017

Multiple comparisons are required to confirm whether or not something is significant if the null hypothesis to test whether the difference between more than three treatments is rejected in a one-way layout. There are both parametric multiple comparison method Tukey (1953) and Nonparametric multiple comparison method based on Kruskal-Wallis (1952).This procedure is applied to a mixed sample of all data and then an average ranking is used for each of three or more treatments. In this paper, a new nonparametric multiple comparison procedure based on joint placements for a one-way layout as extension of the joint placements described in Chung and Kim (2007) was proposed. Monte Carlo simulation is also adapted to compare the family wise error rate (FWE) and the power of the proposed method with previous methods.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.159-167
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• 2017

Observations identified as potential outliers are usually tested for real outliers; however, some outlier detection methods skip a formal test or perform a test using simulated p-values. We introduce test procedures for outliers by testing subsets of potential outliers rather than by testing individual observations of potential outliers to avoid masking or swamping effects. Examples to illustrate methods and a Monte Carlo study to compare the power of the various methods are presented.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.13-20
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• 2014

Phase I Clinical Trials estimate a Maximum Tolerated Dose(MTD). In this paper, an MTD estimation method applied stopping rule is proposed for Phase I Clinical Trials. The suggested MTD estimation method is compared to the Continual Reassessment Method(CRM) method using a Monte Carlo simulation study.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.247-253
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• 1998

In ease of the epidemic Zero-Inflated Poisson model, likelihood ratio test was used for testing epidemic alternatives. Epidemic changepoints were estimated by the method of least squares. It were used for starting points to estimate the maximum likelihood estimators. And several parameters were compared through the Monte Carlo simulations. As a result, maximum likelihood estimators for the epidemic chaagepoints and several parameters are better than the least squares and moment estimators.