图 书 馆
模型、假设和置信限Models, assumptions and confidence limits |
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| 课程网址: | http://videolectures.net/as2010_copas_mac/ |
| 主讲教师: | John Copas |
| 开课单位: | 华威大学 |
| 开课时间: | 2010-10-29 |
| 课程语种: | 英语 |
| 中文简介: |  置信区间反映了我们对关注参数的不确定性，模型反映了我们对数据上下文的假设。这些假设中的某些假设可以由背景知识来证明是正确的，但其他假设则相当武断。统计教科书建议，在建立模型之前，我们应检查模型是否与数据拟合良好（通过使用拟合优度检验或图形诊断）。但是，拟合的模型是否必然意味着良好的置信区间？从模型选择的置信限的稳健性来看，存在一些关于我们在统计学中使用模型和假设的相当基本的问题。 |
| 课程简介: | Confidence intervals reflect our uncertainty about a parameter of interest, and models reflect our assumptions about the context of the data. Some of these assumptions may be justified by background knowledge, but others will be rather arbitrary. Statistics text books advise that before assuming a model we should check that it gives a good fit to the data (by using goodness-of-fit tests or graphical diagnostics). But does a well-fitting model necessarily mean a good confidence interval? Looking at the robustness of confidence limits to model choice suggests some rather basic questions about our use of models and assumptions in statistics. |
| 关 键 词: | 参数; 模型选择; 统计学 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-10-14：zyk |
| 最后编审: | 2020-10-14：zyk |
| 阅读次数: | 37 |