Considering the sample sizes as truncated Poisson random variables in mixed effects models
When applying analysis of variance, the sample sizes may not be previously known, so it is more appropriate to consider them as realizations of random variables. A motivating example is the collection of observations during a fixed time span in a study comparing, for example, several pathologies of...
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| Other Authors: | , , , |
| Format: | article |
| Language: | eng |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10400.6/9064 |
| Country: | Portugal |
| Oai: | oai:ubibliorum.ubi.pt:10400.6/9064 |
| Summary: | When applying analysis of variance, the sample sizes may not be previously known, so it is more appropriate to consider them as realizations of random variables. A motivating example is the collection of observations during a fixed time span in a study comparing, for example, several pathologies of patients arriving at a hospital. This paper extends the theory of analysis of variance to those situations considering mixed effects models. We will assume that the occurrences of observations correspond to a counting process and the sample dimensions have Poisson distribution. The proposed approach is applied to a study of cancer patients. |
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