http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Shang Wenpeng (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
Statistical inference for Cox model under case-cohort design with subgroup survival information
Shang Wenpeng 한국통계학회 2022 Journal of the Korean Statistical Society Vol.51 No.3
With the explosive growth of data, it is a challenge to infer the quantity of interest by combining the existing different research data about the same topic. In the case-cohort setting, our aim is to improve the efficiency of parameter estimation for Cox model by using subgroup information in the aggregate data. So we put forward the generalized moment method (GMM) to use the auxiliary survival information at some critical time points. However, the auxiliary information is likely obtained from other studies or populations, two extended GMM estimators are proposed to account for multiplicative and additive inconsistencies. We establish the consistency and asymptotic normality of the proposed estimators. In addition, the uniform consistency and asymptotic normality of Breslow estimator are also presented. From the asymptotic normality, we show that the proposed approaches are more efficient than the traditional weighted estimating equation method. In particular, if the number of subgroups is equal to one, the asymptotic variance-covariances of the GMM estimators are identical with the weighted score estimate. Some simulation studies and a real data study demonstrate the proposed methods and theories. In the numerical studies, our approaches are even better than the full cohort estimator and the extended GMM methods are robust.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
