• Journal of the Korean Data and Information Science Society
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• 제25권1호
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• pp.211-218
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• 2014

Qu et. al. (2000) proposed the quadratic inference functions (QIF) method to marginal model analysis of longitudinal data to improve the generalized estimating equations (GEE). It yields a substantial improvement in efficiency for the estimators of regression parameters when the working correlation is misspecified. But for the longitudinal data with time-dependent covariates, when the implicit full covariates conditional mean (FCCM) assumption is violated, the QIF can not provide more consistent and efficient estimator than GEE (Cho and Dashnyam, 2013). Lai and Small (2007) divided time-dependent covariates into three types and proposed generalized method of moment (GMM) for longitudinal data with time-dependent covariates. They showed that their GMM type II and GMM moment selection methods can be more ecient than GEE with independence working correlation (GEE-ind) in the case of type II time-dependent covariates. We develop upgraded QIF method for type II time-dependent covariates. We show that this upgraded QIF method can provide substantial gains in efficiency over QIF and GEE-ind in the case of type II time-dependent covariates.

• Journal of the Korean Data and Information Science Society
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• 제24권3호
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• pp.651-658
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• 2013

For the marginal model and generalized estimating equations (GEE) method there is important full covariates conditional mean (FCCM) assumption which is pointed out by Pepe and Anderson (1994). With longitudinal data with time-varying stochastic covariates, this assumption may not necessarily hold. If this assumption is violated, the biased estimates of regression coefficients may result. But if a diagonal working correlation matrix is used, irrespective of whether the assumption is violated, the resulting estimates are (nearly) unbiased (Pan et al., 2000).The quadratic inference functions (QIF) method proposed by Qu et al. (2000) is the method based on generalized method of moment (GMM) using GEE. The QIF yields a substantial improvement in efficiency for the estimator of ${\beta}$ when the working correlation is misspecified, and equal efficiency to the GEE when the working correlation is correct (Qu et al., 2000).In this paper, we interest in whether the QIF can improve the results of the GEE method in the case of FCCM is violated. We show that the QIF with exchangeable and AR(1) working correlation matrix cannot be consistent and asymptotically normal in this case. Also it may not be efficient than GEE with independence working correlation. Our simulation studies verify the result.

• 한국정보과학회논문지:소프트웨어및응용
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• 제33권10호
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• pp.877-885
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• 2006

최근에 대화형 에이전트에서 사용자 질의의 주제 추론을 위하여 베이지안 네트워크가 효과임이 발표되었다. 하지만 베이지안 네트워크는 설계에 있어서 많은 시간이 소요되며, 스크립트(대화를 위한 데이타베이스)의 추가 변경시에는 베이지안 네트워크도 같이 수정해야 하는 번거로움이 있어 대화형 에이전트의 확장성을 저해하고 있다. 본 논문에서는 스크림트로부터 베이지안 네트워크를 자동으로 생성함으로써 베이지안 네트워크를 이용한 대화형 에이전트의 확장성을 높이는 방법을 제안한다. 제안한 방법은 베이지안 네트워크의 구성노드를 계층적으로 설계하고, Noisy-OR gate를 사용하여 베이지안 네트워크의 조건부 확률 테이블을 구성한다. 피험자 10명이 대화형 에이전트를 위한 베이지안 네트워크를 수동 설계한 것과 비교한 결과 제안하는 방법이 효과적임을 알 수 있었다.

• Journal of the Korean Data and Information Science Society
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• 제25권5호
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• pp.1039-1055
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• 2014

검정통계량의 영가설 분포는 모집단 분포에 의존하는데 모집단의 분포를 모를 때 영가설 분포를 검정통계량의 조건부 분포로 대체하여 검정하는 방법을 순열 검정이라고 한다. Strasser와 Weber (1999)는 순열 검정을 통합하는 이론을 마련하였고, Hothorn 등 (2006, 2008)은 그 이론을 R에 내장된 coin 패키지에 구현하였다. coin 패키지에서 조건부 독립성 검정은 총괄적인 형태의 함수인 independence test를 통해서 할 수 있지만 대표적인 독립성 검정은 사용자가 편리하도록 간편한 함수를 별도로 제공하고 있다. 본 논문에서는 Strasser와 Weber (1999)의 순열 검정 방법에 대해 소개하고, coin 패키지에 내장된 15개의 간편 함수에 대해 independence test 함수로 변환하는 절차를 설명하고자 한다. 또한, 정의한 independence test 함수를 써서 실제 자료의 점근 분포와 순열 검정, 정확 검정에 기초한 p-값을 서로 비교하고자 한다.

• 대한골대사학회지
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• 제25권4호
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• pp.251-266
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• 2018

Background: The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC. Methods: We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model. Results: We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC. Conclusions: The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.

• 응용통계연구
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• 제35권2호
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• pp.311-325
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• 2022

0의 값을 과도하게 포함하는 가산자료는 다양한 연구 분야에서 흔히 나타난다. 영과잉 모형은 영과잉 가산자료를 분석하기 위해 가장 일반적으로 사용되는 모형이다. 영과잉 모형에 대한 전통적인 베이지안 추론은 조건부 사후분포의 형태가 폐쇄형 분포로 나타나지 않아 모형 적합 과정이 용이하지 않다는 한계점이 존재했다. 그러나 최근 Pillow와 Scott (2012)과 Polson 등 (2013)이 제안한 폴랴-감마 자료확대전략으로 인해, 로지스틱 회귀모형과 음이항 회귀모형에서 깁스 샘플링을 통한 추론이 가능해지면서, 영과잉 모형에 대한 베이지안 추론이 용이해졌다. 본 논문에서는 베이지안 추론에 기반한 영과잉 음이항 회귀모형을 Min과 Agresti(2005)에서 분석된 약학 연구 자료에 적용해본다. 분석에 사용된 자료는 경시적 영과잉 가산자료로 복잡한 자료 구조를 가지고 있다. 모형 적합 과정에서는 깁스 샘플링을 통한 추론을 수행하기 위해 폴랴-감마 자료확대전략을 사용한다.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.533-540
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• 2000

The problem of information related to I poission experiments, each having a distinct failure rate $\theta$i I=1,2,…,I, is considered. Instead of using a standard exchangeable prior for $\theta$=($\theta$1,$\theta$2,…,$\theta$I), we consider a partition of the experiments and take the $\theta$i's belonging to the same partition subgroup to be exchangeable and the $\theta$i's belonging to distinct subgroups to be independent. And we perform Gibbs sampling approach for Bayesian inference on $\theta$ conditional on a partition. Numerical study using real data is provided.

• Journal of the Korean Statistical Society
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• 제24권1호
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• pp.197-218
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• 1995

We first consider the random censorship model of survival analysis. Efron (1981) introduced two equivalent bootstrap methods for censored data. We propose a new bootstrap scheme, called Method 3, that acts conditionally on the censoring pattern when making inference about aspects of the unknown life-time distribution F. This article contains (a) a motivation for this refined bootstrap scheme ; (b) a proof that the bootstrapped Kaplan-Meier estimatro fo F formed by Method 3 has the same limiting distribution as the one by Efron's approach ; (c) description of and report on simulation studies assessing the small-sample performance of the Method 3 ; (d) an illustration on some Danish data. We also consider the model in which the survival times are censered by death times due to other caused and also by known fixed constants, and propose an appropriate bootstrap method for that model. This bootstrap method is a readily modified version of the Method 3.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1524-1529
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• 2005

Particle filter is a kind of conditional density propagation model. Its similar characteristics to both selection and mutation operator of evolutionary strategy (ES) due to its Bayesian inference rule structure, shows better performance than any other tracking algorithms. When a new object is entering the region of interest, particle filter sets which have been swarming around the existing objects have to move and track the new one instantaneously. Moreover, there is another problem that it could not track multiple objects well if they were moving away from each other after having been overlapped. To resolve reinitialization problem, we use competitive-AVQ algorithm of neural network. And we regard interfarme difference (IFD) of background images as potential field and give priority to the particles according to this IFD to track multiple objects independently. In this paper, we showed that the possibility of real-time object tracking as intelligent interfaces by simulating the deformable contour particle filters.

• Journal of the Korean Data and Information Science Society
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• 제12권1호
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• pp.1-9
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• 2001

In this paper, the problem of information related to I binomial experiments, each having a distinct probability of success ${\theta}_i$, i = 1,2, $\cdots$, I, is considered. Instead of using a standard exchangeable prior for ${\theta}\;=\;({\theta}_1,\;{\theta}_2,\;{\cdots},\;{\theta}_I)$, we con-sider a partition of the experiments and take the ${\theta}_i$'s belonging to the same partition subset to be exchangeable and the ${\theta}_i$'s belonging to distinct subsets to be independent. And we perform Gibbs sampler approach for Bayesian inference on $\theta$ conditional on a partition. Also we illustrate the methodology with a real data.