• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.21 no.1
• /
• pp.164-172
• /
• 1997

Two parameter technique that uses far-field stress and displacement distributions was applied to composite laminates in order to calculate mode II energy release rate, $G_{II}$ . The $G_{II}$ calculated by two parameter technique was compared with that calculated from the crack closure method to inspect the effectiveness of two parameter technique. Sensitivity study of two parameter technique to the crack extension size was also performed. The results showed that both methods produced comparable $G_{II}$ results. In particular, it was found that although the crack closure method was affected by the crack extension size, the two parameter technique was less affected by the crack extension size.

• The Korean Journal of Applied Statistics
• /
• v.8 no.2
• /
• pp.201-216
• /
• 1995

A method of determining the factor numbers is explored in this paper, when data and the factors are binary. We applied a bootstrap approach and proposed a criterion for the method. Simulation results suggest that the proposed method in this paper is very useful in determining the factor numbers for binary data and factors.

• The Korean Journal of Applied Statistics
• /
• v.22 no.2
• /
• pp.341-353
• /
• 2009

We consider sample-size determination problem motivated by comparative clinical trials where patient outcomes are characterized by a bivariate outcome of efficacy and safety. Thall and Cheng (1999) presented a sample size methodology for the case of bivariate binary outcomes. We propose a bivariate Wilcoxon-Mann-Whitney(WMW) statistics for sample-size determination for binary outcomes, and this nonparametric method can be equally used to determine sample sizes of ordinal outcomes. The two methods of sample size determination rely on the same testing strategy for the target parameters but differs in the test statistics, an asymptotic bivariate normal statistic of the transformed proportions in Thall and Cheng (1999) and nonparametric bivariate WMW statistic in the other method. Sample sizes are calculated for the two experimental oncology trials, described in Thall and Cheng (1999), and for the first trial example the sample sizes of a bivariate WMW statistic are smaller than those of Thall and Cheng (1999), while for the second trial example the reverse is true.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.15 no.26
• /
• pp.85-93
• /
• 1992

수작업 또는 컴퓨터에 의하여 신뢰도 방정식을 얻을 때 반드시 그것의 옳고 그름을 검정해 볼 필요가 있다. 본 논문에서는 결합구조에 있어서 이항변수를 사용한 이항 성분으로 이항 시스템을 설계하는 법을 제시하였고 검정하기 위하여 검정 방법 두 가지를 신뢰도 방정식의 정확성을 제안하였다. 아울러 몇 가지 예를 들어 계산상의 효과가 배가되었음을 입증하였다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.36 no.2
• /
• pp.333-340
• /
• 2016

The aim of this study is to specifically understand the characteristics of the crime by empirical analysis for the determining factors that affect determining the crime through the space syntax in Busan. In this study, poisson regression and negative binomial regression were used for accurate analysis. 8 variables that were significant of the total 13 variables. The summary if this study based on the results is as follow. Statistically significant variables are female ratio, over 65 population ratio, administration are and commercial area ratio in characteristics. And the more CCTVs a region has, the lower crime rate it shows. As a results of examing whether space syntax variables can predict crime occurrence places. Space with low connectivity come to be a crime causal factor because they have few other related spaces and thereby have low possibility of sudden appearance of interrupters, which results in low surveillance levels of foot passengers. It will provide the basic data that can contribute to urban planning and implementation of crime prevention aspects.

• The Korean Journal of Applied Statistics
• /
• v.30 no.5
• /
• pp.793-808
• /
• 2017

We compare various variable screening methods on multiclass classification problems when the data is ultrahigh-dimensional. Two different approaches were considered: (1) pairwise extension from binary classification via one versus one or one versus rest comparisons and (2) direct classification of multiclass responses. We conducted extensive simulation studies under different conditions: heavy tailed explanatory variables, correlated signal and noise variables, correlated joint distributions but uncorrelated marginals, and unbalanced response variables. We then analyzed real data to examine the performance of the methods. The results showed that model-free methods perform better for multiclass classification problems as well as binary ones.

• The Korean Journal of Applied Statistics
• /
• v.33 no.1
• /
• pp.25-46
• /
• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.

• The Korean Journal of Applied Statistics
• /
• v.24 no.5
• /
• pp.951-961
• /
• 2011

In this paper, we propose a Bayesian inference using the Markov Chain Monte Carlo(MCMC) method for the zero inflated negative binomial(ZINB) regression model. The proposed model allows the regression model for zero inflation probability as well as the regression model for the mean of the dependent variable. This extends the work of Jang et al. (2010) to the fully defiend ZINB regression model. In addition, we apply the proposed method to a real data example, and compare the efficiency with the zero inflated Poisson model using the DIC. Since the DIC of the ZINB is smaller than that of the ZIP, the ZINB model shows superior performance over the ZIP model in zero inflated count data with overdispersion.

• Communications for Statistical Applications and Methods
• /
• v.19 no.3
• /
• pp.433-450
• /
• 2012

As a worker performs a certain operation repeatedly, he tends to become familiar with the job and complete it in a very short time. That means that the efficiency is improved due to his accumulated knowledge, experience and skill in regards to the operation. Investing time in an output is reduced by repeating any operation. This phenomenon is referred to as the learning curve effect. A learning curve is a graphical representation of the changing rate of learning. According to previous literature, learning curve effects are determined by subjective pre-assigned factors. In this study, we propose a new statistical model to clarify the learning curve effect by means of a basic cumulative distribution function. This work mainly focuses on the statistical modeling of binary data. We employ the Newton-Raphson method for the estimation and Delta method for the construction of confidence intervals. We also perform a real data analysis.

• The Journal of Bigdata
• /
• v.3 no.2
• /
• pp.101-112
• /
• 2018

The purpose of the study is to predict the number of police calls using neural network which is one of the machine learning and negative binomial regression, by using the data of 112 police calls received from Chungnam Provincial Police Agency from June 2016 to May 2017. The variables which may affect the police calls have been selected for developing the prediction model : time, holiday, the day before holiday, season, temperature, precipitation, wind speed, jurisdictional area, population, the number of foreigners, single house rate and other house rate. Some variables show positive correlation, and others negative one. The comparison of the methods can be summarized as follows. Neural network has correlation coefficient of 0.7702 between predicted and actual values with RMSE 2.557. Negative binomial regression on the other hand shows correlation coefficient of 0.7158 with RMSE 2.831. Neural network has low interpretability, but an excellent predictability compared with the negative binomial regression. Based on the prediction model, the police agency can do the optimal manpower allocation for given values in the selected variables.