To model or not to model? competing modes of inference for finite population sampling
To model or not to model? competing modes of inference for finite population sampling
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DOI：
10.1198/016214504000000467
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发表时间：
2004-06-01
影响因子：
3.7
通讯作者：
Little, RJ
中科院分区：
文献类型：
--
作者：
Little, RJ
有限总体抽样也许是统计学中唯一一个主要分析模式是基于随机化分布而不是基于测量变量的统计模型的领域。本文回顾了基于设计的推理和基于模型的推理之间的争论。这两种方法的基本特点说明使用的情况下，推理的平均分层随机样本。基于设计和基于模型的推理调查的优势和劣势进行了讨论。有人建议，模型，考虑到样本设计和弱参数假设，可以产生可靠和有效的推断，在调查设置。这些想法说明使用的问题，从不等概率样本的推断。描述了导致基于设计和基于模型的加权的组合的基于模型的回归分析。
Finite population sampling is perhaps the only area of statistics in which the primary mode of analysis is based on the randomization distribution, rather than on statistical models for the measured variables. This article reviews the debate between design-based and model-based inference. The basic features of the two approaches are illustrated using the case of inference about the mean from stratified random samples. Strengths and weakness of design-based and model-based inference for surveys are discussed. It is suggested that models that take into account the sample design and make weak parametric assumptions can produce reliable and efficient inferences in surveys settings. These ideas are illustrated using the problem of inference from unequal probability samples. A model-based regression analysis that leads to a combination of design-based and model-based weighting is described.