• Journal of Labour Economics
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• v.25 no.1
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• pp.75-95
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• 2002

Using 3 years of panel data on nearly 3,507 households, the Korea Labor Income Panel Survey(KLIPS) data, the authors measure the determinants of poverty and unemployment, and the extents to which poverty influenced unemployment. The probit analysis of unemployment shows that unemployment probability is lower, when male, lower age and is higher, high-school and over junior college, work duration is over 3 years. The probit analysis of poverty shows that poverty probability is lower, when male, higher education level, longer career. specially unemployment and social insurance is determinants of increasing poverty. Bivariate probit model of unemployment and poverty also provides similar findings to those probit analysis and shows an evidence of the influence of unemployment on poverty along with the positive role of social welfare policy such that social welfare receipt reduces the impact of unemployment on poverty.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.115-127
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• 2023

Multinomial probit model is a popular model for multiclass classification and choice model. Markov chain Monte Carlo (MCMC) method is widely used for estimating multinomial probit model, but its computational cost is high. However, it is well known that variational Bayesian approximation is more computationally efficient than MCMC, because it uses subsets of samples. In this study, we describe multinomial probit model with Gaussian process classification and how to employ variational Bayesian approximation on the model. This study also compares the results of variational Bayesian multinomial probit model to the results of naive Bayes, K-nearest neighbors and support vector machine for the UCI mice protein expression level data.

• Environmental and Resource Economics Review
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• v.11 no.4
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• pp.609-632
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• 2002

국민소득이 빠르게 증가함에 따라 1990년대 이후 가정용 난방연료의 소비구조 역시 크게 변화하고 있다. 본 연구는 에너지 및 교통수요분석에 많이 사용되는 Multinomial Probit 모형을 이용하여 가정용 난방연료의 선택 행태를 분석하였다. 모형의 추정방법으로는 베이지안(Baysian) 방법론에 의한 Gibbs Sampling기법 (McColluch et al., 2000)을 이용하여 Multinomial probit 모형에서 선택대안이 3개 이상일 경우 발생할 수 있는 추정상의 어려움을 극복하였다. 한국가구패널조사(KHPS) 자료를 이용하여 서울과 경기도 대도시 지역을 대상으로 분석한 결과, 석유와 천연가스가 연탄에 비해 더 밀접한 상호 대체관계를 가지고 있는 것으로 나타났다. 또한 소득이 높은 가구일수록 천연가스에 대한 선호도가 더 높은 것으로 나타나서 향후 공급망 확대에 따라 난방연료용 가스 소비가 더욱 늘어날 것으로 예상된다.

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

• Transportation Technology and Policy
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• v.7 no.1
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• pp.37-45
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• 2010

• The Journal of the Korea Contents Association
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• v.10 no.5
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• pp.210-219
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• 2010

Consumers differ in both horizontally and vertically. Market segmentation aims to divide horizontally different (or heterogeneous) consumers into more similar (or homogeneous) small segments. A specific consumer, however, may differ in vertically. He (or she) may belong to a different market segment from another one where he (or she) belonged to before. In consumer panel data, the vertical difference can be observed by his (or her) choice among brand alternatives are changing over time. The consumer's vertical difference has been defined as 'dynamics'. In this research, we have developed a binary probit model with random-walk coefficients to capture the consumer's dynamics. With an application to a consumer panel data, we have examined how have the random-walk coefficients changed over time.

• Environmental and Resource Economics Review
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• v.16 no.3
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• pp.427-449
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• 2007

This study estimates the recreation benefits of rafting on the Hantan River. A choice experiment is conducted and the economic values of controlling water stream and water quality are estimated. Both the conditional logit and the multinomial pro bit models are estimated. This study rejects the IIA assumption of the conditional log it model and supports using a more flexible model such as the multinomial probit model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.2
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• pp.108-117
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• 2009

The South Korean government is looking for a solution to the ever-growing problems of traffic congestion, and surging international oil prices: the use of the humble bicycle to get to places. However, Many people feel inconvenient using bicycle because of the insufficient bicycle infrastructure and lack of the safety and connectivity between existing pathways. In this study, bicycle level of service model using ordered probit model is developed considering safety, convenience, connectivity, and factors that affect bicycle LOS. The ordered probit model would be recommended for the research which relates in choice, preference and strength etc. Bicycle level of service criteria is calculated by applying this model reflecting bicyclist's point of view. The model which develops from this research which accomplishes a bicycle level of service evaluation and represent alternative solution to encourage bicyclist. It is believed that the proposed model would be greatly utilized in bicycle network planning, bicycle road and facility alternatives testing, projects funding priority.

• Environmental and Resource Economics Review
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• v.12 no.3
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• pp.469-485
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• 2003

This paper reports on the results of epidemiological investigation of daily health effects in the elderly associated with daily exposure to particulate matters in Korea. Our main focus is on the potential difference in health effects between PM10 and PM2.5. While the Korean environmental authority has set an ambient standard for PM10, the government currently does not monitor PM2.5, which has no national standard. A daily data on respiratory symptoms as well as PM concentrations are collected for a total of 120 days. Using a probit model, we find statistically significant negative health effects of PM2.5 on respiratory symptoms among the nonsmoking elderly, while PM10 does not show such effects from the estimation. This result suggests that, for air quality regulatory purposes, PM2.5 can be a more appropriate air pollutant than PM10.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1017-1028
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• 2011

When it is not easy to decide the credit scoring for some loan applicants, credit evaluation is postponded and reserve to ask a specialist for further evaluation of undecided applicants. This undecided inference is one of problems that happen to most statistical models including the biostatistics and sportal statistics as well as credit evaluation area. In this work, the undecided inference is regarded as a missing data mechanism under the assumption of MNAR, and use the bivariate probit model which is one of sample selection models. Two undecided inference methods are proposed: one is to make use of characteristic variables to represent the state for decided applicants, and the other is that more accurate and additional informations are collected and apply these new variables. With an illustrated example, misclassification error rates for undecided and overall applicants are obtainded and compared according to various characteristic variables, undecided intervals, and thresholds. It is found that misclassification error rates could be reduced when the undecided interval is increased and more accurate information is put to model, since more accurate situation of decided applications are reflected in the bivariate probit model.