응용통계연구 (The Korean Journal of Applied Statistics)

  • 제28권2호
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  • Pages.231-239
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  • 2015
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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영과잉 공간자료의 분석

Zero In ated Poisson Model for Spatial Data

  • 한준희 (양산부산대학교병원 연구통계지원실) ;
  • 김창훈 (부산대학교 의학전문대학원)
  • Han, Junhee (Research And Statistical Support, Research Institute of Convergence for Biomedical Science and Technology, Pusan National University Yangsan Hospital) ;
  • Kim, Changhoon (Department of Preventive Medicine, Pusan National University School of Medicine)
  • 투고 : 2015.03.16
  • 심사 : 2015.03.31
  • 발행 : 2015.04.30
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초록

가산자료(counts data)를 적합 하는 경우 보통 포아송 모형이 가장 먼저 고려된다. 과산포 문제가 있을 경우도 유사 포아송(quasi Poisson) 모형이나 음이항(Negative binomial) 모형으로 대부분 설명이 가능하다. 하지만, 가산자료 중에는 포아송분포를 가정한 기대 빈도 이상으로 많은 0이 관측되는 자료가 있고 이를 영과잉(Zero inflated) 가산 자료라고 부른다. 영과잉 가산자료를 설명하기 위해 영과잉 포아송(ZIP) 모형이나 영과잉 음이항(ZINB) 모형을 이용할 수 있다. 더 나아가 영과잉 가산자료가 공간상관관계까지 있을 경우 영과잉 문제뿐만 아니라 유의할 수 있는 공간효과까지 고려해야하고 이를 위해 혼합효과모형(mixed effects model)이 고려 될 수 있다. 본 연구에서 사용된 2004년 기준 부산시 남성동별 갑상선암 발생자수 자료를 이용하여, 일반선형 포아송모형, 영과잉 포아송모형, 공간 영과잉 포아송모형을 적합하여 비교해보았다.

A Poisson model is the first choice for counts data. Quasi Poisson or negative binomial models are usually used in cases of over (or under) dispersed data. However, these models might be unsuitable if the data consist of excessive number of zeros (zero inflated data). For zero inflated counts data, Zero Inflated Poisson (ZIP) or Zero Inflated Negative Binomial (ZINB) models are recommended to address the issue. In this paper, we further considered a situation where zero inflated data are spatially correlated. A mixed effect model with random effects that account for spatial autocorrelation is used to fit the data.

키워드

참고문헌

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