...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Australian & New Zealand journal of statistics >MISSING DATA MECHANISMS FOR ANALYSING LONGITUDINAL DATA WITH INCOMPLETE OBSERVATIONS IN BOTH RESPONSES AND COVARIATES
【24h】

MISSING DATA MECHANISMS FOR ANALYSING LONGITUDINAL DATA WITH INCOMPLETE OBSERVATIONS IN BOTH RESPONSES AND COVARIATES

机译：响应和变量的观测不完整的纵向数据分析的缺失数据机制

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Missing observations in both responses and covariates arise frequently in longitudinal studies. When missing data are missing not at random, inferences under the likelihood framework often require joint modelling of response and covariate processes, as well as missing data processes associated with incompleteness of responses and covariates. Specification of these four joint distributions is a nontrivial issue from the perspectives of both modelling and computation. To get around this problem, we employ pairwise likelihood formulations, which avoid the specification of third or higher order association structures. In this paper, we consider three specific missing data mechanisms which lead to further simplified pairwise likelihood (SPL) formulations. Under these missing data mechanisms, inference methods based on SPL formulations are developed. The resultant estimators are consistent, and enjoy better robustness and computation convenience. The performance is evaluated empirically though simulation studies. Longitudinal data from the National Population Health Survey and Waterloo Smoking Prevention Project are analysed to illustrate the usage of our methods.
机译：在纵向研究中，经常在响应和协变量中缺少观察结果。当缺失数据不是随机丢失时，似然框架下的推论通常需要对响应和协变量过程进行联合建模，以及与响应和协变量的不完整性相关的缺失数据过程。从建模和计算的角度来看，这四个关节分布的规范都是不平凡的问题。为了解决这个问题，我们采用成对似然公式，避免了三阶或更高阶关联结构的规范。在本文中，我们考虑了三种特定的缺失数据机制，这些机制导致了进一步简化的成对似然（SPL）公式。在这些缺失的数据机制下，开发了基于SPL公式的推理方法。结果估计量是一致的，并享有更好的鲁棒性和计算便利性。通过模拟研究对性能进行了经验评估。分析了国家人口健康调查和滑铁卢吸烟预防项目的纵向数据，以说明我们方法的使用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号