• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.63-68
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• 2003

One of the widely accepted assumptions in many statistical problem is that the underlying distribution is symmetric. Though a large number of nonparametric test are available in the literature for this problem, very few procedures focuses on the distributional structure when the symmetry assumption is rejected. Yanagimoto and Sibuya (1972) provided the various types of asymmetric distributional structure, positive biasedness, namely. In this paper we consider the test of symmetry against several new positive biasedness restrictions which are stronger than Yanagimoto and Sibuya's type II bias but weaker than type IV (III) bias.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.479-486
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• 1999

In this paper we propose the test statistic for ordered alternatives on multiple ranked set samples. Since the proposed test statistic is Page type its asymptotic properties are easily obtained. From the simulation works we calculate the power of test statistic($P_{RSS}$) under the underlying distributions such as uniform normal double exponential logistic and Cauchy distribution.

• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.577-588
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• 1996

The Bayes factor provides a possible hierarchical Bayesian approach for studying the change point problems. A hypothesis for testing change versus no change is considered using predictive distributions. When the underlying distribution is in one-parameter exponential family with conjugate priors, Bayes factors are investigated to the hypothesis above. Finally one example is provided .

• International Journal of Reliability and Applications
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• v.7 no.1
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• pp.13-25
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• 2006

A new class of life distributions is studied. This class is defined based on comparing the residual life time to the whole life in the moment generating function order giving 'the new better than used in the moment generating function order ageing class $(NBU_{mgf})$'. Fundamental properties of this class are given including some closure properties and characterizations. Finally, we consider new results about comparisons of age and block replacement policies when the underlying distribution belongs to $NBU_{mgf}$ aging classes.

• Journal of the Korean Data and Information Science Society
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• v.3 no.1
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• pp.33-46
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• 1992

The set of eigenvectors of the second moment matrix and the mean vector are the measures of orientation for a distribution supported on the unit sphere. Bootstrap confidence cone for the eigenvector is constructed and the consistency of this method is discussed. The performance of our bootstrap cone for the eigenvector is compared with that of the asymptotic confidence cones for two measures under the parametric assumptions for the underlying distributions and that of the bootstrap cone for the mean vector by Monte Carlo simulation.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.413-423
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• 2005

Consider the problem of estimating the underlying regression function from a set of noisy data which is contaminated by a long tailed error distribution. There exist several robust smoothing techniques and these are turned out to be very useful to reduce the influence of outlying observations. However, no matter what kind of robust smoother we use, we should choose the smoothing parameter and relatively less attention has been made for the robust bandwidth selection method. In this paper, we adopt the idea of robust location parameter estimation technique and propose the robust cross validation score functions.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.345-358
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• 2012

In a parametric sample selection model, the distribution assumption is critical to obtain consistent estimates. Conventionally, the normality assumption has been adopted for both error terms in selection and main equations of the model. The normality assumption, however, may excessively restrict the true underlying distribution of the model. This study introduces the $S_U$-normal distribution into the error distribution of a sample selection model. The $S_U$-normal distribution can accommodate a wide range of skewness and kurtosis compared to the normal distribution. It also includes the normal distribution as a limiting distribution. Moreover, the $S_U$-normal distribution can be easily extended to multivariate dimensions. We provide the log-likelihood function and expected value formula based on a bivariate $S_U$-normal distribution in a sample selection model. The results of simulations indicate the $S_U$-normal model outperforms the normal model for the consistency of estimators. As an empirical application, we provide the sample selection model for car ownership and a car expense relationship.

• Journal of Korean Society for Quality Management
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• v.44 no.1
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• pp.167-180
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• 2016

Purpose: The problem for the traditional control chart is that it is unable to monitor the very small fraction of nonconforming and the underlying distribution is the normal distribution. $Z_p$ control chart is useful where it controls the vert small fraction on nonconforming. In this study, we will design the $Z_p$ control chart in order to use under non-normal process. Methods: $Z_p$ is calculated not by failure rate based on attribute data but using variable data. Control limit for non-normal $Z_p$ control chart is designed based on ${\alpha}$-risk calculated by cumulative distribution function of Burr distribution. ${\beta}$-risk, which is for performance evaluation, obtains in the Burr distribution's cumulative distribution function and control limit. Results: The control limit for non-normal $Z_p$ control chart is designed based on Burr distribution. The sensitivity can be checked through ARL table and OC curve. Conclusion: Non-normal $Z_p$ control chart is able to control not only the very small fraction of nonconforming, but it is also useful when $Z_p$ distribution is non-normal distribution.

• Journal of Korean Society of Rural Planning
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• v.20 no.3
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• pp.11-20
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• 2014

This research aims to identify regional inequitable development through the analysis of facilities distribution pattern. This study describes the concepts underlying the application of the Gini's coefficient and decomposition method to measure the regional inequitable development in Sun-chang County, Jeonbuk Province, Korea. We used the facility data surveyed for three years, from 2010 to 2012 for facility distribution pattern that RDA surveyed. These data have been serviced on the web. The Lorenz Curve presents a graphical view of the inequitable facility distribution and the Gini's Coefficients quantifies the distribution pattern. And furthermore, Gini Decomposition represents intra regional inequalities. These applied techniques can describe how the local development affects other district and change regional inequalities.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.5
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• pp.60-65
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• 2019

Since nanofluids (NFs), which are a mixture of a small amount of nanoparticles and a bulk liquid solvent, were first proposed by Stephen Choi at the Argonne National Lab in 1995, they have been considered for use in many technical studies of power cooling systems and their practical application due to their high thermal conductivity and heat transfer coefficients compared to conventional coolants. Although nanofluids are a well-known form of engineering fluid that show great promise for use in future cooling systems, their underlying physics as demonstrated in experiments remain unclear. One proven method of determining the heat transfer performance of nanofluids is measuring the concentration of nanoparticles in a mixture. However, it is experimentally inefficient to build testbeds to systematically observe particle distributions on a nanoscale. In this paper, we demonstrate the distribution of nanoparticles under a temperature gradient in a solution using molecular dynamics simulations. First, temperature profiles based on substrate temperature are introduced. Following this, the radial pair distribution functions of pairs of nanoparticles, solvents, and substrates are calculated. Finally, the distribution of nanoparticles in different heating regions is determined.