• Journal of the Korean Institute of Gas
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• v.12 no.1
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• pp.42-47
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• 2008

The demand of gas as an eco-friendly energy source has being increased. With the demand of gas, the use of gas is also increased, so injury and loss of life by the explosion and fire have been increasing every year. Hence the influence on over-pressure caused by Vapor Cloud Explosion in enclosure of experimental booth was calculated by using the Hopkinson's scaling law and damage effect by the accident to a human body was estimated by applying the probit model. As a result of the damage estimation conducted by using the probit model, both the damage possibility of explosion overpressure to human over 3 meters away and that of overpressure to tympanum rupture over 25 meters away from the explosion shows nothing.

• 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.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.493-505
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• 1997

The analysis of binary data appears to many areas such as statistics, biometrics and econometrics. In many cases, data are often collected in which some observations are incomplete. Assume that the missing covariates are missing at random and the responses are completely observed. A method to Bayesian analysis of the binary regression model with incomplete data is presented. In particular, the desired marginal posterior moments of regression parameter are obtained using Meterpolis algorithm (Metropolis et al. 1953) within Gibbs sampler (Gelfand and Smith, 1990). Also, we compare logit model with probit model using Bayes factor which is approximated by importance sampling method. One example is presented.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.147-158
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• 2019

The purpose of this study is to identify differences based on demographic characteristics and travel-related characteristics: first, whether travelers used a domestic travel agency and second whether travelers purchased a full-package travel program. A sample selection probit model was used to provide simultaneous evaluation of the different characteristics of outbound travelers. The present study investigates how tourists make decisions based on two travel-pattern choices. It then goes on to explore the characteristics of outbound travelers from South Korea. The data is drawn from a nationwide survey in South Korea, and a total of 859 surveys were used for analysis. Due to the interdependent nature of the choices, a sample selection probit model was used to estimate outbound tourists' use of domestic travel agency and purchase of full travel package. Significant determinants of domestic travel agency use are identified as age, gender, marital status, party size, children, length of travel, and travel distance, while those of full travel package purchase are age, marital status, and travel purpose. Estimated results provide manifestations of differing travel needs of outbound travelers. the results of this study demonstrate differences between travel-agency users and full-package travel-program consumers and provide determinants that affect the purchase of full-package travel.

• The Journal of Fisheries Business Administration
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• v.55 no.1
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• pp.37-53
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• 2024

In the post-COVID-19, the food industry is rapidly reshaping its market structure toward online distribution. Rapid delivery system driven by large distribution platforms has ushered in an era of online distribution of fresh seafood that was previously limited. This study surveyed 1,000 consumers nationwide to determine their online seafood purchasing behaviors. The research methodology used factor analysis of consumer lifestyle and Heckman's ordered probit sample-selection model. The main results of the analysis are as follows. First, quality, freshness, selling price, product reviews from other buyers, and convenience are particularly important considerations when consumers purchase seafood from online shopping. Second, online retailers and the government must prepare measures to expand seafood consumption by considering household characteristics and consumer lifestyles. Third, it was analyzed that consumers trust the quality and safety of seafood distributed online platforms. It is not possible to provide purchase incentives to consumers who consider value consumption important, so improvement measures are needed. The results of this study are expected to provide implications on consumer preferences to online platforms, seafood companies, and producers, and can be used to establish future marketing strategies.

• 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.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.613-635
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• 2001

This paper proposes a skewed multivariate probit model for analyzing a correlated binary response data with covariates. The proposed model is formulated by introducing an asymmetric link based upon a skewed multivariate normal distribution. The model connected to the asymmetric multivariate link, allows for flexible modeling of the correlation structure among binary responses and straightforward interpretation of the parameters. However, complex likelihood function of the model prevents us from fitting and analyzing the model analytically. Simulation-based Bayesian inference methodologies are provided to overcome the problem. We examine the suggested methods through two data sets in order to demonstrate their performances.

• Journal of Korean Society for Quality Management
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• v.44 no.3
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• pp.553-564
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• 2016

Purpose: The purpose of this study was to propose useful suggestions by analyzing reliability of guided missile using field data in Military industry. Methods: The collected data from Defense industry company and the military were analyzed using probit analysis which is non-linear model because field data contain binary variable. Results: The results of this study are as follows; It was found that the effect of time was significant. It takes about 12.4 years when 10st percentile of guided missiles are not working and it takes about 18.6 years when 50st percentile of guided missiles are not working. It was found that period between 10years to 15years comes less than reliability 0.0. Conclusion: Periodical check needs to extend from 4 year to 10 year partially. Early LOT need to check per 4 year and follow-up LOT extend the period of check to 10 year by reflecting the result of reliability.

• International Area Studies Review
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• v.16 no.3
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• pp.53-73
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• 2012

In this study we investigate the ability of the yield spread to predict economic recessions in two Asian economies. For our purpose we use the data from two emerging economies (South Korea and Thailand) that are also known for their openness in terms of exports and imports. We employ both two-regime Markov-Switching model (MS) and three-regime MS model to estimate the probability of recessions during Asian crisis. We found that the yield spread is confirmed to be a reliable recession predictor for Thailand but not for South Korea. The three-regime MS model is better for capturing the Asian financial crisis than two-regime MS model. We also tried to find the duration of economic expansions and recessions. We tested the hypothesis of asymmetric movements of business cycles. The MS results are also compared with that of the standard probit model for comparison. The MS model does not significantly improve the forecasting ability of the yield spread in forecasting business cycles.

• Journal of Technology Innovation
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• v.5 no.1
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• pp.67-89
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• 1997

This study investigates the factors determining accounting method for R&D costs (capitalizevs. expense) in Korea. Using agency theory and other economic factors, probit and regression model have been developed to distinguish between firms choosing different accounting alternatives for R&D costs. The results are consistent to debt contract, R&D burden and regulation hypotheses both in probit and regression analysis. The size variable has opposite sign in univariate t-test and probit analysis, which may be due to the differences of political environment between Korea and the US. Generally, the results are consistent to those of previous research. The evidence suggests that larger firms with higher leverage and larger burden of R&D costs are more likely to capitalize R&D costs, while regulated firms are more likely to expense R&D costs.