• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.136-136
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• 1998

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.483-497
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• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

• Ocean Systems Engineering
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• v.9 no.3
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• pp.287-328
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• 2019

Comprehensive understanding of the flood risk assessments via frequency analysis often demands multivariate designs under the different notations of return periods. Flood is a tri-variate random consequence, which often pointing the unreliability of univariate return period and demands for the joint dependency construction by accounting its multiple intercorrelated flood vectors i.e., flood peak, volume & durations. Selecting the most parsimonious probability functions for demonstrating univariate flood marginals distributions is often a mandatory pre-processing desire before the establishment of joint dependency. Especially under copulas methodology, which often allows the practitioner to model univariate marginals separately from their joint constructions. Parametric density approximations often hypothesized that the random samples must follow some specific or predefine probability density functions, which usually defines different estimates especially in the tail of distributions. Concentrations of the upper tail often seem interesting during flood modelling also, no evidence exhibited in favours of any fixed distributions, which often characterized through the trial and error procedure based on goodness-of-fit measures. On another side, model performance evaluations and selections of best-fitted distributions often demand precise investigations via comparing the relative sample reproducing capabilities otherwise, inconsistencies might reveal uncertainty. Also, the strength & weakness of different fitness statistics usually vary and having different extent during demonstrating gaps and dispensary among fitted distributions. In this literature, selections efforts of marginal distributions of flood variables are incorporated by employing an interactive set of parametric functions for event-based (or Block annual maxima) samples over the 50-years continuously-distributed streamflow characteristics for the Kelantan River basin at Gulliemard Bridge, Malaysia. Model fitness criteria are examined based on the degree of agreements between cumulative empirical and theoretical probabilities. Both the analytical as well as graphically visual inspections are undertaken to strengthen much decisive evidence in favour of best-fitted probability density.

• 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.

• Journal of Korea Multimedia Society
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• v.10 no.9
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• pp.1185-1196
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• 2007

To enhance competitiveness of game industry and added value, it needs scientific research and bases that suggest satisfaction standard which quantitatively evaluates satisfaction to develop games which have high satisfaction for demanders. Specially, scoring of satisfaction factors and estimation of population distribution are important task. This allows which a game have high or low satisfaction compared to other games. Also we predict improvable factors and technical aspects to promote satisfaction. For it, in this paper we discuss ways to normalization of score distribution for satisfaction factors and estimate its density function using parametric density estimation by simulation.

• Journal of Magnetics
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• v.20 no.4
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• pp.405-412
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• 2015

In this study, we describe the effects of changing the magnet shape of permanent magnets (PMs) in a rotor Halbach-array PM generator for reciprocating free piston generator applications. More specifically, the rectangular-shaped magnets were replaced by the trapezoidal-shaped magnets. The initial design, an analytical magnetic field solution of rectangular shaped magnets, is presented and air-gap magnetic flux density and thrust force were estimated. The results were compared to the finite element analysis (FEA) showing excellent agreement. Using FEA, the effect of the shape of the magnets on the flux density and thrust force waveforms is analyzed. Moreover, the proportion of the Halbach array in the machine was optimized by the means of a parametric search. The results obtained from the analytical calculations and FEA were validated by comparing to those of Radial-array PM generator.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.04a
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• pp.55-59
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• 1997

Investigation is performed on the stability of general form of dynamic system whose damping and stiffness are varying in irregular manner along time, which is a preliminary result in the course of research on the characteristic and the control of the stochastic system. The governing equation of the 'parametric' system is derived via F-P-K approach in stochastic sense. The influence on the stability due to the magnitude of auto power spectral density and cross power spectral density of random variation of system parameters is studied and the region is surveyed.

• Water Engineering Research
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• v.2 no.1
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• pp.1-10
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• 2001

The frequency analyses for the precipitation data in Korea were performed. We used daily maximum series, monthly maximum series, and annual series. For nonparametric frequency analyses, variable kernel estimators were used. Nonparametric methods do not require assumptions about the underlying populations from which the data are obtained. Therefore, they are better suited for multimodal distributions with the advantage of not requiring a distributional assumption. In order to compare their performance with parametric distributions, we considered several probability density functions. They are Gamma, Gumbel, Log-normal, Log-Pearson type III, Exponential, Generalized logistic, Generalized Pareto, and Wakeby distributions. The variable kernel estimates are comparable and are in the middle of the range of the parametric estimates. The variable kernel estimates show a very small probability in extrapolation beyond the largest observed data in the sample. However, the log-variable kernel estimates remedied these defects with the log-transformed data.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.87-94
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• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.169-185
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• 2022

The urbanization and increasing rate of population demands effective means of transportation system (basement and tunnels) as well as high-rise building (resting on piled foundation) for accommodation. Therefore, it unavoidable to construct basements (i.e., excavation) nearby piled foundation. Since the basement excavation inevitably induces soil movement and stress changes in the ground, it may cause differential settlements to nearby piled raft foundation. To understand settlement and load transfer mechanism in the piled raft due to excavation-induced stress release, numerical parametric studies are carried out in this study. The effects of excavation depths (i.e., formation level) relative to piled raft were investigated by simulating the excavation near the pile shaft (i.e., H_e/L_p=0.67), next to (H_e/L_p=1.00) and below the pile toe (H_e/L_p=1.33). In addition, effects of sand density and raft fixity condition were investigated. The computed results have revealed that the induced settlement, tilting, pile lateral movement and load transfer mechanism in the piled raft depends upon the embedded depth of the diaphragm wall. Additional settlement of the piled raft due to excavation can be account for apparent loss of load carrying capacity of the piled raft (ALPC). The highest apparent loss of piled raft capacity ALPC (on the account of induced piled raft settlement) of 50% was calculated in in case of H_e/L_p = 1.33. Furthermore, the induced settlement decreased with increasing the relative density from 30% to 90%. On the contrary, the tilting of the raft increases in denser ground. The larger bending moment and lateral force was induced at the piled heads in fixed and pinned raft condition.