• 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.

• 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.

• 한국ITS학회:학술대회논문집
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• v.2007 no.10
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• pp.149-155
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• 2007

• 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.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2012.05a
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• pp.45-53
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• 2012

본 연구에서는 정부의 R&D 투자를 받는 기업들의 특성을 규명하였다. 정부의 투자 행태를 반영하기 위해 순위 프로빗 모형(ordered probit)을 사용하였으며, 기업의 특성과 과제의 특성을 함께 고려함으로써 정부 정책의 실효성과 정합성을 분석하였다. 분석 결과 대기업, 매출액이 큰 기업, R&D집약도가 높은 기업, 벤처기업 등이 정부 R&D 투자를 많이 받는 것으로 나타났다. 기업특성과 과제 특성을 함께 분석한 결과, 중소기업이 신성장동력, 녹색기술 등에 대한 투자를 많이 받은 것으로 나타났다. 기업에 대한 투자가 개발연구 위주로 추진된 것은 합당하나, 대기업 중심, 성장기 기술 중심의 편중 현상은 개선의 여지가 있는 것으로 보인다.

• 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.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 Data and Information Science Society
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• v.27 no.3
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• pp.653-661
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• 2016

In this paper, we provide a suitable optimal exponent in the generalized Pythagorean theorem and propose to use the logistic model & the probit model to estimate the winning rate in Korean professional baseball league. Under a criterion of root-mean-square-error (RMSE), the efficiencies of the proposed models have been compared with those of the Pythagorean theorem. We use the team historic win-loss records of Korean professional baseball league from 1982 to the first half of 2015, and the proposed methods show slight outperformances over the generalized Pythagorean method under the criterion of RMSE.

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.47-55
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• 2005

In recent years, the rapid growth of vehicles have increased traffic crashes. Since they can cause the economic losses and have put the life qualify in danger, there should be numerous efforts to reduce traffic crashes. To reduce traffic crashes, this research seeks to improve the safety of intersections by analysing causations of injury severity with Ordered Probability Model. This research applied the Ordered Probit Model, which assumes that ${\epsilon}_i$(random error) is normally distributed, for model calibration and used $p^2$ (likelihood ratio) and $x^2$ (Chi-square) for model selection. The results show that minor road traffic, heavy vehicle rates, major and minor right-turn rates, presence of lightings, speed limits, instructive line for left-turn traffic are significant factors affecting crash severities at signalized intersections.

• 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.