• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.683-691
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• 2012

In $4{\times}4$ graeco-latin square design, powers of rank transformed statistic for testing the main effect are superior to powers of parametric statistic without regard to the effect structure with equally or unequally spaced effect levels as well as the type of population distributions such as exponential, double exponential, normal and uniform distribution. As numbers of block effect or effect sizes are decreased, powers of rank transformed statistic are much higher than powers of parametric statistic. In case that block effects are smaller than a main effect or one block effect is higher than other block effects, powers of rank transformed statistic are much higher than powers of parametric statistic in $4{\times}4$ graeco-latin square design with three block effects and one main effect.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.327-340
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• 2008

This paper derives joint and conditional Lagrange multiplier tests based on information matrix for testing functional form and/or the presence of autocorrelation in a regression model. Small sample properties of these tests are assessed by Monte Carlo study and comparisons are made with LM tests based on Hessian matrix. The results show that the proposed $LM_E$ tests have the most appropriate finite sample performance.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.3
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• pp.458-471
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• 2013

This paper addresses the limitation of a map comparison metric entitled Figure of Merit through employing a simple land change model. The metric was originally designed to overcome limitations of other existing statistics, such as Kappa, when assessing predictive accuracy of land change models. A series of comparisons between null and predicted outcomes at multiple resolutions as well as a multi-resolution Figure of Merit analysis techniques of validation are compared for spatially segregated calibration and validation datasets. The Figure of Merit at the null resolution in this paper was 57%, although future research must be done to determine if this was simply a coincidence. A Figure of Merit greater than 50% would seem to represent a "Resolution of Merit" in that the Figure of Merit at that resolution becomes greater than the error. Thus, these two metrics should be used in tandem to assess predictive accuracy of a land change model.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.123-136
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• 2015

Science has developed with great achievements after Galileo's discovery of the law depicting a relationship between observable variables. However, many natural phenomena have been better explained by models including unobservable random effects. A mixed effect model was the first statistical model that included unobservable random effects. The importance of the mixed effect models is growing along with the advancement of computational technologies to infer complicated phenomena; subsequently mixed effect models have extended to various statistical models such as hierarchical generalized linear models. Hierarchical likelihood has been suggested to estimate unobservable random effects. Our special issue about mixed effect models shows how they can be used in statistical problems as well as discusses important needs for future developments. Frequentist and Bayesian approaches are also investigated.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.315-323
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• 1999

이 논문에서는 국내 우편통계의 현황에 대하여 간략히 언급하고 있으며 접수 우편물량에 대한 기술통계와 배달 우편물량에 대한 추정을 다루고 있다. 성장곡선모형을 이용하여 접수 우편물량에 대한 예측을 논의하였고 성장곡선모형 중에서 삼차곡선이 매우 잘 적합시키는 것을 확인할 수 있었다. 또한 체신청별 배달 우편물량을 체신청별 접수 우편물량과 마코프 연쇄에서 나타나는 전이행렬을 이용하여 추정하였으며 이는 표본이 비추정의 기본 개념과 일맥 상통함을 발견하였다.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.335-340
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• 2003

최근 들어 생존분석 기법이 여러 분야에서 관심을 모으고 있을 뿐 아니라 생존자료를 분석하기 위한 여러 패키지들도 개발되어 연구되고 있다. 본고에서는 생존분석의 여러 모형을 간략히 소개하고 생존자료를 분석하기 위하여 널리 사용되고 있는 패키지인 SAS, SPSS, STATA의 기능을 찾아보고 그들의 특징을 비교 조사할 것이다.

• Journal of the Korean Statistical Society
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• v.1 no.1
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• pp.38-64
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• 1973

본논문은 한국 주요도시의 인구 유출입현상을 통계적으로 구명하고 구명된 결과를 기초로 하여 앞으로의 인구를 예측하는 데에 목적이 있다. 이를 위하여서 간단한 마아코프 연쇄를 이용한 인구이동모형을 선정하고 센서스 자료를 적용하였다. 인구의 예측에는 흔희 단순 L-곡선(simple logistic curve)을 사용하는 것이 일반적인 방법이나 도시인구의 유출입과 같은 사회적 인구증가율을 내포하고 있는 경우에는 적당한 방법이라고 할 수 없겠다. 본논문은 5개의 장과 부록으로 구성되어 있으며 2장에서는 모형과 기초자료의 분석이 있고 3장에서는 모형의 적용, 4장에서는 도시 인구흡인력 등에 관련된 제반 통계량을 추정하였다. 마지막 장은 결과의 평가로 삼았다.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.25-28
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• 2002

비선형모형분석의 초기 단계에서 초기값(starting value, initial parameter value)를 결정하는 문제는 비선형모형의 모수추정을 위한 반복기법의 수렴속도나 국소값(local minimum)문제에 영향을 주게 된다. 본 논문을 통하여 탐색적 자료분석이 초기값를 결정하는 데 도움을 줄 수 있음을 보이고자 한다.

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.131-140
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• 1994

Box-Jenkins 시계열 분석에서 모형검진을 위한 통계량으로 잔차의 자기상관함수를 이용한 Box와 Pierce(1970)의 포트맨토우 검정과 Ljung과 Box(1978)의 변형된 포트맨토우 검정을 Basawa(1987)가 제안한 예측오차를 이용한 모형 검진 방법과 비교, 분석하였다. 시뮬레이션 연구를 수행하여 경험적 평균, 분산 및 유의 수준을 비교하여 과대적합의 방법을 이용하여 검정력을 비교하였다.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.