응용통계연구 (The Korean Journal of Applied Statistics)

  • 제30권2호
  • /
  • Pages.203-213
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  • 2017
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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가우시안 코플라를 이용한 반복측정 이변량 자료의 조건부 결합 분포 추정

Estimation of the joint conditional distribution for repeatedly measured bivariate cholesterol data using Gaussian copula

  • 투고 : 2016.04.18
  • 심사 : 2016.10.12
  • 발행 : 2017.04.30
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초록

우리는 이변량 경시적 자료의 조건부 결합 분포를 추정하기 위하여 회귀 모형과 코플라 모형을 연구하였다. 주변 분포의 추정을 위하여 시변 변환 모형을 고려하였고, 이변량 반응변수 각각에 대한 주변 분포를 가우시안 코플라를 이용하여 결합하여 조건부 결합 분포를 추정하였다. 우리가 제안한 모형은 조건부 평균 모형만으로 자료를 설명하기 어려운 경우에 적용될 수 있다. 시변 변환 모형과 가우시안 코플라 모형을 결합한 본 논문의 방법은 반복 측정된 이변량 경시적 자료에 대한 모형화가 용이하며 해석하기 쉬운 장점이 있다. 우리는 본 논문의 방법을 반복 측정된 이변량 콜레스테롤 자료를 분석하는데 적용하여 보았다.

We study estimation and inference of joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. We consider a class of time-varying transformation models and combine the two marginal models using Gaussian copulas to estimate the joint models. Our models and estimation method can be applied in many situations where the conditional mean-based models are inadequate. Gaussian copulas combined with time-varying transformation models may allow convenient and easy-to-interpret modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.

키워드

참고문헌

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