• 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 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.

• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.167-177
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• 2017

In this paper, we propose the role of job crafting in the relationship between leadership and organizational effectiveness in voluntarily carrying out each member's assigned tasks. This study surveyed the manufacturing, construction, service industries in Seoul and Gyeonggi province, identified the type of leadership they recognized, and empirically analyzed the organizational effectiveness of leadership. The purpose of this paper is to grasp the types of leadership acknowledged by the industry of manufacturing, construction, and service, and also to empirically analyze the organizational effectiveness of the leadership. The study measures the organizational effectiveness in terms of the job satisfaction, organizational commitment, and organizational citizenship behavior while classifying the leadership into coaching leadership, transformational leadership, and sensible leadership. In addition, the strictness of the analysis is imposed by estimating the simple least square model and ranking probit model. The results of the least square model is summarized as the following. Regardless of the different defining terms of organizational effectiveness, transformational leadership was shown to have the greatest organizational effectiveness. Sensible leadership positively effected job satisfaction whereas coaching leadership positively effected job satisfaction and organizational effectiveness. Compared to transformational leadership and coaching leadership, the impact of the sensible leadership was very much limited. The result of the ranking probit model is summarized as the following. First, sensible leadership had a positive impact on the member's job satisfaction and organizational commitment. Second, regarding the organizational citizenship behavior, coaching leadership showed greater impact than transformational leadership. This results contradicts the results from the simple least square model. If similar studies were to be conducted in the future, two models and the results must be compared. Third, as the leadership score increases by 1 point, there is greater possibility of having more than 4 points for all job satisfaction, organizational commitment, organizational citizenship behavior. Lastly, the analysis proves that job crafting has the mediation effect between the leadership and organizational effectiveness.

• 한국ITS학회:학술대회논문집
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• 2008.11a
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• pp.193-198
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• 2008