• 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.

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

• 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.

• 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 Korea Technology Innovation Society
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• v.13 no.3
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• pp.513-537
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• 2010

According to the "FDI-as-market-discipline" hypothesis, inward FDI acts as a mechanism of change in market structure affecting innovative activities of domestic firms. We used panel KIS data for testing this hypothesis. Binary probit estimation shows that, in contrast to the German case of Bertschek (1995), FDI is insignificant in Korean case for explaining product innovation. 1his result maybe comes from the fact that the industries in Korea are more monopolistic or oligopolistic than those of Germany. Using panel data, we tried random parameter estimation using matrix weighted average of GLS and OLS. The result shows different estimates from cross-section outcome and panel estimation with parameter homogeneity, so we can infer large parameter heterogeneity across firms. But, interpretation for FDI variable is similar across panel and cross-section estimation.

• Korean Journal of Social Welfare
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• v.33
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• pp.287-313
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• 1997

• 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.

• Journal of the Korean Institute of Gas
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• v.17 no.3
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• pp.1-7
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• 2013

The influence of the over-pressure caused by Explosion in gas station was calculated by using the Hopkinson's scaling law and injury effect by accident to buildings and human bodies was estimated by applying the probit model. As a result, the injury estimation was conducted by using the probit model for leakage 10% of 20ton storage tank. The separate distances from LPG station for building(damage) and human(lung hemorrhage to death) are 260 and 30 meters, respectively.

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