A Comparison of Two Sampling Inference Systems between Design-Based Inference and Model-Based Inference

摘要：

Traditional randomization-theory-based sampling methodologies believe that the values of variables on population elements are fixed and the randomness embodies in sample selection. Its inference for the population depends on sampling design. Model-based inference thinks that population elements values are random, and the finite population is a random sample drawn from a superpopulation. Inference for the population depends on modeling. This paper compares the two methods on application situation, weights and assumed conditions, and points out that model based inference have important application value in complex sampling.

作者: HE Benlan JIN Yongjin GONG Hongyu

作者单位: School of Statistics, Renmin University of China, P.R.China, 100872 The Center for Applied Statistics Science, Renmin University of China, P.R.China, 100872

会议类型: 国际会议

会议名称: The Fourth International Institute of Statistics & Management Engineering Symposium(第四届(2011)国际统计与管理工程研讨会 IISMES 2011)

会议地点: 大连

会议语种:英文

页码: 1373-1379

在线出版日期: 2011-07-24（万方平台首次上网日期，不代表论文的发表时间）