• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.295-304
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• 2019

This article deals with threshold-asymmetric volatility models for over-dispersed and zero-inflated time series of count data. We introduce various threshold integer-valued autoregressive conditional heteroscedasticity (ARCH) models as incorporating over-dispersion and zero-inflation via conditional Poisson and negative binomial distributions. EM-algorithm is used to estimate parameters. The cholera data from Kolkata in India from 2006 to 2011 is analyzed as a real application. In order to construct the threshold-variable, both local constant mean which is time-varying and grand mean are adopted. It is noted via a data application that threshold model as an asymmetric version is useful in modelling count time series volatility.

• International Journal of Highway Engineering
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• v.16 no.2
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• pp.119-129
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• 2014

PURPOSES : The purpose of this study is to develop the U-turn accident model at 4-legged signalized intersections in urban areas. METHODS : In order to analyze the characteristics of the accidents which are associated with U-turn operation at 4-legged signalized intersections in urban areas and develop an U-turn accident model by regression analysis, the tests of overdispersion and zero-inflation are conducted about the dependent variables of number of accidents and EPDO (Equivalent Property Damage Only). RESULTS : As their results, the Poisson model fits best for number of accident and the ZIP (Zero Inflated Poisson) fits best for EPOD, the variables of conflict traffic, width of opposing road, traffic passing speed are adopted as independent variable for both models. The variables of number of bus berths and rate of U-turn signal time at which the U-turn is permitted are adopted as independent variable only for EPDO. CONCLUSIONS : These study results suggest that U-turn would be permitted at the intersection where the width of opposing road is wider than 11.9 meters, the passing vehicle speed is not high and U-turn operation is not hindered by the buses stopping at bus stops.

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

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2841-2852
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• 2018

This study examines regression modeling method using zero-inflated distribution in relation to estimation of exposure distribution required in advertisement media planning. Exposure distribution is the percentage of audiences that are exposed each time the ad is repeated. Such an exposure distribution plays a very important role in providing basic information necessary for calculating various indicators for quantitatively measuring the advertising effect. Especially, due to the decrease of advertising price and the spread of various media, the frequency of the advertisement or the broadcasting of specific advertisements has been greatly increased compared to the past. As a result, the frequency of exposure is relatively decreasing. In this situation, the number of individuals who are not exposed to the media, that is, are not exposed to advertising structurally is increasing. This research proposes advertising exposure distribution models using a zero-inflated regression model, and conducts a comparative study using actual cases.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.363-376
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• 2012

Excessive zeroes are often observed in ordinal categorical response variables. An ordinary ordered Probit model is not appropriate for zero-inflated data especially when there are many different sources of generating 0 observations. In this paper, we apply a two-stage zero-inflated ordered Probit (ZIOP) model which incorporate the zero-flated nature of data, propose a Bayesian analysis of a ZIOP model, and apply the method to alcohol consumption data collected by the National Bureau of Statistics, Korea. In the first stage of a ZIOP model, a Probit model is introduced to divide the non-drinkers into genuine non-drinkers who do not participate in drinking due to personal beliefs or permanent health problems and potential drinkers who did not drink at the time of the survey but have the potential to become drinkers. In the second stage, an ordered probit model is applied to drinkers that consists of zero-consumption potential drinkers and positive consumption drinkers. The analysis results show that about 30% of non-drinkers are genuine non-drinkers and hence the Korean alcohol consumption data has the feature of zero-inflated data. A study on the marginal effect of each explanatory variable shows that certain explanatory variables have effects on the genuine non-drinkers and potential drinkers in opposite directions, which may not be detected by an ordered Probit model.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.493-504
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• 2017

We often observe count data that exhibit over-dispersion, originating from too many zeros, and under-dispersion, originating from too few zeros. To handle this types of problems, the zero-altered distribution model is designed by Ghosh and Kim in 2007. Their model can control both over-dispersion and under-dispersion with a single parameter, which had been impossible ever. The dispersion type depends on the sign of the parameter ${\delta}$ in zero-altered distribution. In this study, we demonstrate the role of the dispersion type parameter ${\delta}$ through the data of the number of births in Korea. Employing both the chi-square statistic and the Kolmogorov statistic for goodness-of-fit, we also explained any difference between the theoretical distribution and the observed one that exhibits either over-dispersion or under-dispersion. Finally this study shows whether the test statistics for goodness-of-fit show any similarity with the role of the dispersion type parameter ${\delta}$ or not.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.274-279
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• 2000

This paper is concerned with the orthotropic problem of diaphragm-type air springs which consist of rubber linings, nylon reinforced rubber composite and bead ring. The analysis is carried out with a finite element method developed to consider the orthotropic properties, geometric nonlinearity using four-node degenerated shell element with reduced integration. Physical stabilization scheme is used to control the zero-energy modes of the element. An inflation analysis and a lateral deformation analysis of an air spring are carried out. Numerical analysis results demonstrate the variation of the outer diameter, the fold height, the vertical force and the lateral force.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.155-158
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• 2023

한국 무역수지는 지난해 12월, 올해 1월과 4월부터 현재까지 8개월간 연속 적자를 기록했다. 2000년 이후 무역수지가 2개월 이상 지속된 적은 2008년 세계경기 위기 이후 처음이다. 수출 부분도 23개월 연속 증가세를 이어오다가 반도체 등 주력 품목 수출액이 급감하면서 2년 만에 감소로 돌아섰다. 무역수지에 악영향을 끼치는 요인으로는 러시아·우크라이나 전쟁 장기화, 주요국 통화 긴축 등에 따른 글로벌 경기둔화 영향으로 각국의 수입 수요가 둔화되고 있는 상황이다. 특히, 우리 최대 수출국인 중국의 수입시장 위축과 최대 수출 품목인 반도체 가격 하락이 우리 수출에 부담으로 작용했다. 위와 같은 상황을 고려하면 당분간 수출 감소·무역적자 흐름에서 벗어나기 어려울 전망이다. 본 논문에서는 무역수지가 어떠한 요인들에 의해서 결정되고 이러한 요인들의 영향에 의하여 무역수지가 어떻게 변동하는지를 실증적으로 분석하고 시사점을 도출하고자 한다.

• Journal of Distribution Science
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• v.14 no.10
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• pp.109-116
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• 2016

Purpose - This study investigates the purchase pattern relating to soybean products in Korea. Specifically, the effect of branding based on a regional name was analyzed in terms of consumer purchase frequencies. The primary purpose of this study is to understand why family characteristics affect product selection for a regional brand in the soybean food category. Research design, data, and methodology - We used data collected by the Rural Development Administration (RDA) of Korea. The RDA has monitored agricultural food consumers for years in order to obtain purchase records. Panel participants live in regions near the capital city of Seoul, Korea. Examining data from January 2010 to May 2016, 667 families were selected for analysis. The final data set was 1,335,402. Each purchase item by each individual family was aggregated to a countable weekly observation. To analyze the data set quantitatively, zero-inflation regression was adopted, which was appropriate to avoid biases from overly dispersed observations. Results - We hypothesized the effects of regional branding from the viewpoint of the family characteristics. The first hypothesis was that the number of children would be positively associated with the purchase of a regional brand of soybean products. The result strongly supported this hypothesis. The second hypothesis was that the number of family members would be negatively associated with the purchase of the soybean products of a regional brand. Based on empirical analysis, we concluded that this hypothesis was partially supported. The third hypothesis was the presence of an interaction effect between the number of children and the family size, which was supported by the results. As a supplementary analysis, we also tested mean-variance differences in terms of categories and regional branding with corporate branding. Conclusion - The results of this study provide insights for regional branding strategies in agricultural food management. This study appears to be one of the seminal studies trying to analyze purchase patterns from longitudinal observations. In addition, this study adopted variables characterizing family lifestyle. This study confirmed that children and family size should be considered when soybean product brands are introduced.

• The Journal of the Korea Contents Association
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• v.17 no.4
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• pp.444-460
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• 2017

The duration of volunteering can be analyzed in terms of commitment and attachment. Previous studies have investigated the duration of volunteering predominantly from the perspective of commitment. Alternatively, this study focuses on the concept of attachment and investigates the characteristics of those who volunteer habitually over their whole life, regardless of the regularity and the intensity of the volunteer work. In so doing, the study attempts to identify factors associated with the attachment to volunteering. Data came from a sample of 8,415 participants, ages over twenty who responded to all the surveys of the Korea Welfare Panel Study, from Wave 1 to 10. Zero-inflated negative bionomial regression model was employed to analyze the total number of volunteering in the past ten years. Findings show that people with high attachment to volunteering were those with religion, less education, and a strong sense of reciprocity. Based on the findings, we provide the practical implications for the improved operation and management of volunteer organizations.