• 보건행정학회지
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• 제3권2호
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• pp.159-176
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• 1993

The utilization of outpatient care services involves two steps of sequential decisions. The first step decision is about whether to initiate the utilization and the second one is about how many more visits to make after the initiation. Presumably, the initiation decision is largely made by the patient and his or her family, while the number of additional visits is decided under a strong influence of the physician. Implication is that the analysis of the outpatient care utilization requires to specify each of the two decisions underlying the utilization as a distinct stochastic process. This paper is concerned with the number of physician visits, which is, by definition, a discrete variable that can take only non-negative integer values. Since the initial visit is considered in the analysis of whether or not having made any physician visit, the focus on the number of visits made in addition to the initial one must be enough. The number of additional visits, being a kind of count data, could be assumed to exhibit a Poisson distribution. However, it is likely that the distribution is over dispersed since the number of physician visits tends to cluster around a few values but still vary widely. A recently reported study of outpatient care utilization employed an analysis based upon the assumption of a negative binomial distribution which is a type of overdispersed Poisson distribution. But there is an indication that the use of Poisson distribution making adjustments for over-dispersion results in less loss of efficiency in parameter estimation compared to the use of a certain type of distribution like a negative binomial distribution. An analysis of the data for outpatient care utilization was performed focusing on an assessment of appropriateness of available techniques. The data used in the analysis were collected by a community survey in Hwachon Gun, Kangwon Do in 1990. It was observed that a Poisson regression with adjustments for over-dispersion is superior to either an ordinary regression or a Poisson regression without adjustments oor over-dispersion. In conclusion, it seems the most approprite to assume that the number of physician visits made in addition to the initial visist exhibits an overdispersed Poisson distribution when outpatient care utilization is studied based upon a model which embodies the two-part character of the decision process uderlying the utilization.

• Communications for Statistical Applications and Methods
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• 제26권6호
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• pp.527-538
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• 2019

This paper considers the issue of obtaining the optimal design in Poisson regression model when the sample size is small. Poisson regression model is widely used for the analysis of count data. Asymptotic theory provides the basis for making inference on the parameters in this model. However, for small size experiments, asymptotic approximations, such as unbiasedness, may not be valid. Therefore, first, we employ the second order expansion of the bias of the maximum likelihood estimator (MLE) and derive the mean square error (MSE) of MLE to measure the quality of an estimator. We then define DM-optimality criterion, which is based on a function of the MSE. This criterion is applied to obtain locally optimal designs for small size experiments. The effect of sample size on the obtained designs are shown. We also obtain locally DM-optimal designs for some special cases of the model.

• 대한예방의학회:학술대회논문집
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• 대한예방의학회 2002년도 제54차 추계 학술대회 연제집
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• pp.202-203
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• 2002

• 한국도로학회논문집
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• 제14권4호
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• pp.83-91
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• 2012

PURPOSES : This study deals with Rotary by Accident Occurrence Location. The purpose of this study is to develop the accident models of rotary by location. METHODS : In pursuing the above, this study gives particular attentions to developing the appropriate models using multiple linear, Poisson and negative binomial regression models and statistical analysis tools. RESULTS : First, four multiple linear regression models which are statistically significant(their $R^2$ values are 0.781, 0.300, 0.784 and 0.644 respectively) are developed, and four Poisson regression models which are statistically significant(their ${\rho}^2$ values are 0.407, 0.306, 0.378 and 0.366 respectively) are developed. Second, the test results of fitness using RMSE, %RMSE, MPB and MAD show that Poisson regression model in the case of circulatory roadway, pedestrian crossing and others and multiple linear regression model in the case of entry/exit sections are appropriate to the given data. Finally, the common variable that affects to the accident is adopted to be traffic volume. CONCLUSIONS : 8 models which are all statistically significant are developed, and the common and specific variables that are related to the models are derived.

• 응용통계연구
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• 제19권3호
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• pp.505-519
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• 2006

셀 수 있는 이산 자료(discrete count data)에 대한 분석은 여러 분야에서 활용되고 있지만 영(zero)을 과도하게 포함하고 있는 영과잉 자료는 자료의 성격상 포아송 분포를 따르지 못할 때가 있어 분석에 어려움이 따른다. Zero-Inflated Poisson(ZIP)모형은 이런 어려움을 극복하기 위하여 영에 대한 점확률을 가지는 분포와 포아송 분포를 합성하여 과도한 영과 영이 아닌 자료를 설명하는 모형이다. 설명 변수가 존재할 때는 포아송 분포 부분에서 반응변수의 평균과 공변량사이에 로그선형 연결함수를 사용한 Zero-Inflated Poisson Regression(ZIPR)모형이 사용될 수 있다. 본 논문에서는 Markov Chain Monte Carlo 기법을 이용한 ZIPR모형의 베이지안 추론방법을 제안하고, 이를 실제 구강위생 자료에 적용하며 다른 모형들과 비교한다. 그 결과 베이지안 추론 방법을 적용한 영과잉 모형의 추정오차가 다른 모형들의 추정오차보다 작았고, 예측치가 더 정확했다는 점에서 우수함을 알 수 있었다.

• Communications for Statistical Applications and Methods
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• 제11권2호
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• pp.381-397
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• 2004

The marginal likelihood has become an important tool for model selection in Bayesian analysis because it can be used to rank the models. We discuss the marginal likelihood for Poisson regression models that are potentially useful in small area estimation. Computation in these models is intensive and it requires an implementation of Markov chain Monte Carlo (MCMC) methods. Using importance sampling and multivariate density estimation, we demonstrate a computation of the marginal likelihood through an output analysis from an MCMC sampler.

• Journal of the Korean Data and Information Science Society
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• 제23권4호
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• pp.851-858
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• 2012

We consider a bivariate Poisson regression model to analyze discrete count data when two dependent variables are present. We estimate the regression coefficients as sociated with several safety countermeasures. We use Markov chain and Monte Carlo techniques to execute some computations. A simulation and real data analysis are performed to demonstrate model fitting performances of the proposed model.

• 콘크리트학회논문집
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• 제16권2호
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• pp.195-204
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• 2004

기존의 다축응력 상태의 콘크리트 크리프 실험으로부터 제안된 푸아송비에 대한 연구결과는 서로 큰 차이를 나타내고 있다. 측정된 변형률로부터 계산된 푸아송비는 작은 실험 오차에 의해서도 매우 민감하며, 이러한 민감성은 푸아송비의 시간에 따른 변화와 응력상태에 따른 경향을 파악하는 데 있어 큰 어려움을 초래한다. 따라서 이러한 연구결과의 불일치를 해결하고 신뢰성 있는 결과를 도출하기 위해서 새로운 분석방법이 요구된다. 이 연구는 다축응력 상태의 크리프 실험결과에 대한 새로운 분석방법으로 미세평면 모델을 적용하였다. 미세평면 상에서 체적과 편차컴플라이언스에 대한 수학적 모델로는 이중지수 법칙을 사용하였다. 체적과 편차성분의 컴플라이언스는 여섯 개의 변수로 구성되며 실험결과를 최적으로 모사하는 변수를 최적화 기법으로부터 구하였다. 여섯 변수에 대한 회귀분석결과로 부터 계산된 푸아송비는 시간에 따라 변화하였다. 또한 시간에 따라 푸아송비가 일정하다는 조건에서 네 변수를 결정하였으며 이 때의 회귀분석결과와 실험 측정값 사이의 오차는 여섯 변수가 사용된 회귀분석결과의 오차에 비해 다소 크게 나타났다. 네 변수에 대한 회귀분석결과로부터 얻은 시간에 따라 일정한 푸아송비는 큰 오차 없이 실제의 구조해석에 유용하게 사용될 수 있을 것으로 판단된다.

• Journal of the Korean Statistical Society
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• 제31권4호
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• pp.509-518
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• 2002

This paper is concerned with semiparametric Bayesian analysis of the proportional intensity regression model of the Poisson process for multiple event time data. A nonparametric prior distribution is put on the baseline cumulative intensity function and a usual parametric prior distribution is given to the regression parameter. Also we allow heterogeneity among the intensity processes in different subjects by using unobserved random frailty components. Gibbs sampling approach with the Metropolis-Hastings algorithm is used to explore the posterior distributions. Finally, the results are applied to a real data set.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2023년도 추계학술대회
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• pp.120-121
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• 2023

최근에 해운산업의 수요와 공급이 지속적으로 일치하지 않으면서 불균형 현상이 이어지고 있다. 이에 따라 해운선사들은 선박의 공급량을 조절하기 위해 블랭크 세일링을 실시하며 수요와 공급의 균형을 맞추고 있다. 블랭크 세일링은 화물 운송을 지연시키는 부정적인 연쇄효과를 발생시키기 때문에 본 연구에서는 포아송 회귀분석을 이용하여