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9 Poisson regression
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Published:May 1996
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Abstract
To summarize the different approaches to statistical inference and decisionmaking, an analogy with practice in the medical profession may be useful. This profession has an accumulated knowledge of many diseases - the statistical models. A task of the doctor is to discover which one applies to a particular patient. When a radically new drug is developed, doctors will conduct a randomized study to compare its effect to a control group to determine what possible effects the medication has - direct likelihood inference. However, before the drug is released on the market,the hypothesis that it has no effect, or that it is inferior to existing medication, must be rejected in favour of a positive effect, optimizing a long term criteria in the population - frequentist decision-making. Once a doctor begins to use the drug on patients, information obtained from these studies can be incorporated into the diagnoses to decide what is the optimal treatment - Bayesian decision-making.
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