• 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 Labour Economics
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• v.29 no.2
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• pp.145-169
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• 2006

This study analyze the effect on wage, labor mobility by using Craft II Class certification out of National skill certification. In this article, we used the parametric and nonparametric method. In the former we used IV that the fraction of certification by occupation by firm scale to solve the selection problem. In the latter, it's used matching method and kernel regression. The paper shows that certification effect on wage has about 5.1~9.9%. The result of analysis between certification and labor mobility indicates better certification effects on long term tenure to the same firm than certification effects on wage from labor mobility. Also, we knew that the employee which have no certification relative is difficult to be established in the same workplace.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.923-932
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• 2008

We are likely to face complex multivariate data which can be characterized by having a non-trivial correlation structure. For instance, omitted covariates may simultaneously affect more than one count in clustered data; hence, the modeling of the correlation structure is important for the efficiency of the estimator and the computation of correct standard errors, i.e., valid inference. A standard way to insert dependence among counts is to assume that they share some common unobservable variables. For this assumption, we fitted correlated random effect models considering multilevel model. Estimation was carried out by adopting the semiparametric approach through a finite mixture EM algorithm without parametric assumptions upon the random coefficients distribution.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.777-792
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• 1998

병원의 임상연구실험에서 종종 환자들의 치료에 따른 병세의 호전상태를 여러단계로 분류하여 상이한 치료방법에 대한 치료효과간의 차이론 알고자 하는 경우가 있다. 이와 같이 다중상태의 생존자료를 분석하기 위해서 본 논문에서는 semi-Markov 모형에 Cox 회귀모형을 적용하여 회귀계수와 기저생존함수를 추정하고 이를 바탕으로 반응확률함수를 추정하였다. 그리고 본 논문의 결과를 실제 임상실험에서 얻어진 자료에 적용하여 분석하였다.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.304-304
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• 2006

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.331-343
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• 2014

We propose two estimating procedures to analyze clustered interval-censored data with an informative cluster size based on a marginal model and investigate their asymptotic properties. One is an extension of Cong et al. (2007) to interval-censored data and the other uses the within-cluster resampling method proposed by Hoffman et al. (2001). Simulation results imply that the proposed estimators have a better performance in terms of bias and coverage rate of true value than an estimator with no adjustment of informative cluster size when the cluster size is related with survival time. Finally, they are applied to lymphatic filariasis data adopted from Williamson et al. (2008).

• Journal of Labour Economics
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• v.42 no.3
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• pp.39-74
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• 2019

In this paper, the wage equations of local labor markets for college graduates in Korea are estimated by Dahl(2002)'s methodology to correct for selection bias. The results suggest that the variations of coefficients in wage equations across the local labor markets are mostly remained after correcting for selection bias. The gender wage gap is hardly affected by selection bias. The variations of return to education and the major premium are reduced about 18% and 11% respectively. Meanwhile, the selection bias is negligible in the national capital region, which suggests that college graduates prefer the national capital region regardless of their gender, level of education, and major.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.255-268
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• 1992

We propose an iterated interpolation approach for the estimation fo time series parameters in the presence of outliers. The proposed approach iterates the parameter estimation stage and the outlier detection stage until no further outliers are detected. For the detection of outliers, interpolation diagnostic is applied, where the atypical observations by the one-step-ahead predictor instead of downweighting is also proposed. The performance of the proposed estimation methods is compared with other robust estimation methods by simulation study. It is observed that the iterated interpolation approach performs reasonably well is general, especially for single AO case and large $\phi$ in absolute values.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.449-458
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• 2007

Bipolar disorder, also known as manic-depressive illness, is a brain disorder that causes unusual shifts in person's mood, energy, and ability to function. Compared with manic episode, the depression episode causes more serious results such as restless, loss of interest or pleasure, or thoughts of death or suicide and the cure rate of depression episode is lower than that of manic episode. Furthermore, a long term use of antidepressants in bipolar patients may result in manic episode. Our interest is to investigate the effect of antidepressant on switch of moods of bipolar patients and to estimate the transition probabilities of switch between moods, depression and (hypo) manic. In this study, three approaches are applied in terms of multi state model. Parametric model is applied using left censoring data and nonparametric model is implemented under illness-death model with counting process. In order to estimate the effect of covariates, a multiplicative model is used. These all methods have similar results.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.467-482
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• 2020

We investigate a monitoring procedure for the early detection of parameter changes in location-scale time series models. We introduce a detector for monitoring procedure based on modified residual cumulative sum (CUSUM). The asymptotic properties of the monitoring procedure are established under the null and alternative hypotheses. Simulation results and data analysis are also provided for illustration.