• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.111-115
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• 2003

소지역 모형들은 고정된(fixed)효과와 랜덤 효과를 포함하는 일반적 선형 혼한 모형의 특별한 경우로 간주될 수 있다. 소지역 평균이나 종계는 고정된 효과와 랜덤 효과의 일치 결합으로 표현될 수 있다. 블록 대각 공분산 구조를 갖는 선형 혼합모형(mixed model) 아래서 EBLUP은 실재문제에 있어서 많이 소지역 모형에 응용된다. 설계 가중값(design weight) 들에 의존하고 설계-일치(design consistency) 성질을 만족하는 Pseudo-EBLUP 추정량들은 소지역추정에서 합해지면 (aggregated) 사후-수정(post-adjustment)없이 벤치마킹 성질을 만족한다.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.411-420
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• 2005

In this study, the MIVQUE estimators of variance components for the one-way random model with unbalanced data are investigated. In order to compare the efficiency of MIVQUE estimators obtained by using three priori estimates, the Empirical Quantile Dispersion Graphs (EQDGs) are used. From the results of Monte-Carlo study, the MIVQUE estimator using ${\sigma}^2_{\alpha}\;=\;0\;and\;{\sigma}^2_{varraho}=1$ as the priori estimate performs well relative to other estimators.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.151-158
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• 2015

This paper discusses how to use projections for the analysis of data from balanced incomplete block designs. A model is suggested as a matrix form for the interblock analysis. A second set of treatment effects can be found by projections from the suggested interblock model. The variance and covariance matrix of two estimated vectors of treatment effects is derived. The uncorrelation of two estimated vectors can be verified from their covaraince structure. The fitting constants method is employed for the calculation of block sum of squares adjusted for treatment effects.

• Journal of Labour Economics
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• v.40 no.1
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• pp.69-97
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• 2017

This paper estimates the public and private sector wage gap trend from 2000 to 2014 using 'Korean Labor and Income Panel Study.' We account for unobserved fixed effect by using 1st differencing log wage in order to allow the gap to vary over time. Standard OLS estimates present the public sector wage is 10% higher than private sector on average. Moreover, the public sector wage premium displays the inverted V shape: sharply increasing up to 2006 and decreasing from 2007 to 2014. However, after controlling unobserved fixed effect, the public sector wage premium disappears and does not display the inverted V shape any more.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.501-515
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• 2022

Despite the stylized statistical features of returns of financial returns such as fat-tailed distribution and leverage effect, no stochastic volatility models that can explicitly capture these features have been presented in the existing frequentist approach. we propose an approximate parameterization of stochastic volatility models that can explicitly capture the fat-tailed distribution and leverage effect of financial returns and a maximum likelihood estimation of the model using Langrock et al. (2012)'s hidden Markov model approximation in a frequentist approach. Through extensive simulation experiments and an empirical analysis, we present the statistical evidences validating the efficacy and accuracy of proposed parameterization.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.273-287
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• 2001

90년대 말 이후 우리나라가 극심한 경제 불황을 겪으면서 구조조정을 c통한 시장기능의 회복에 관심이 집중되고 있다. Pigou에 의하면 소비함수를 통한 실질자산효과로 불황하에도 시장의 가격기구를 통해 장기균형에 달성이 가능하다는 것이다. 본 논문은 이와 같이 실질자산효과를 Phillips-Hansen의 FM-OLS 공적분추정으로 실증분석을 수행하였는데 분석결과에 의하면 우리나라의 경우 Pigou가 주장한 실질자산효과가 거의 없는 것으로 나타나 실질자산효과가 정책적으로 고려의 대상이 되는지의 여부는 충분히 검토되어야 할 것이다. 더구나 실질자산효과의 크기는 물가의 신축성의 정도에 따라 달라지는데 우리나라의 경우 물가의 신축성에 많은 제약이 있기 때문에 현실적으로는 실질자산효과가 더욱 축소되어 나타날 것으로 보인다. 결과적으로 실질자산효과에 의한 소비증가가 IS곡선을 이동시킬 만큼 충분치 못할 것으로 판단된다.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.173-185
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• 2019

Causal questions are prevalent in scientific research, for example, how effective a treatment was for preventing an infectious disease, how much a policy increased utility, or which advertisement would give the highest click rate for a given customer. Causal inference theory in statistics interprets those questions as inferring the effect of a given intervention (treatment or policy) in the data generating process. Causal inference has been used in medicine, public health, and economics; in addition, it has received recent attention as a tool for data-driven decision making processes. Many recent datasets are observational, rather than experimental, which makes the causal inference theory more complex. This review introduces key concepts and recent trends of statistical causal inference in observational studies. We first introduce the Neyman-Rubin's potential outcome framework to formularize from causal questions to average treatment effects as well as discuss popular methods to estimate treatment effects such as propensity score approaches and regression approaches. For recent trends, we briefly discuss (1) conditional (heterogeneous) treatment effects and machine learning-based approaches, (2) curse of dimensionality on the estimation of treatment effect and its remedies, and (3) Pearl's structural causal model to deal with more complex causal relationships and its connection to the Neyman-Rubin's potential outcome model.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.487-494
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• 2012

This paper discusses a method for getting a basis set of estimable functions of model parameters in a two-way fixed effects model. Since the fixed effects model has more parameters than those that can be estimated, model parameters are not estimable. So it is not possible to make inferences for nonestimable functions of parameters. When the assumed model of matrix notation is reparameterized by the estimable functions in a basis set, it also discusses how to use projections for the estimation of estimable functions.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.957-981
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• 2017

A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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• pp.315-320
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• 2000

본 연구에서는 수리 가능한 시스템에서 고장 강도와 수리 효과에 대한 모수 추정 문제를 다룬다. 시스템이 노후화로 인한 고장이 발생할 경우 최소수리가 행해지고 계획된 예방정비에서는 비례적 수명 감소가 이루어지는 수명 데이터에 대해서 고장 강도 함수의 모수와 정비의 수리효과를 추정하기 위해서 최대 우도 함수 방법을 이용한다. 또한 유전자 알고리즘을 이용해서 우도 함수를 최대화시키는 절차를 개발하고 수치 예제를 나타낸다.