图 书 馆
一个计数数据模型：灵活的组件对象模型泊松分布A Flexible Model for Count Data: The COM-Poisson Distribution |
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| 课程网址: | http://videolectures.net/solomon_shmueli_count_data/ |
| 主讲教师: | Galit Shmuéli |
| 开课单位: | 印度商学院 |
| 开课时间: | 2012-09-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Count data arise in many contexts, from word lengths to traffic volume to number of bids in online auctions, and generally in many event-counting applications. Yet, there is a scarcity of statistical models for such data. The Poisson distribution is the most popular distribution for modeling count data, yet it is constrained by its equi-dispersion assumption, making it less than ideal for modeling real data that often exhibit over-dispersion or under-dispersion. The COM-Poisson distribution is a two-parameter generalization of the Poisson distribution that allows for a wide range of over-dispersion and under-dispersion. It also contains the Bernoulli and geometric distributions as special cases, and as a member of the exponential family has useful statistical properties. This distribution's flexibility and special properties have prompted a fast growth of methodological and applied research in various fields. In this talk, I will introduce the COM-Poisson distribution and regression model and mention several other COM-Poisson models that have been published thus far. I will also describe applications of the COM-Poisson in various areas including disclosure limitation, marketing, transportation and linguistics. |
| 关 键 词: | 计数; 统计模型; 假设约束 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-29：wuyq |
| 阅读次数: | 43 |