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1.卜瓦松迴歸(Poisson Regression)

http://web.thu.edu.tw/sljeng/www/Datamining/predic...

2.Poisson regression

In statistics, the Poisson regression model attributes to a response variable Y a Poisson distribution whose expected value depends on a predictor variable x (written in lower case because the model treats x as non-random, typically in the following way:

(where "log" means natural logarithm). Poisson regression models are generalized linear models with the logarithm as the (canonical) link function, and the Poisson distribution function.

If Yi are independent observations with corresponding values xi of the predictor variable, then a and b can be estimated by maximu likelihood if the number of distinct x values is at least 2. The maximum-likelihood estimates lack a closed-form expression and must be found by numerical methods.

3.Poisson regression in practice

Poisson regression is appropriate when the dependent variable is a count, for instance of events such as the arrival of a telephone call at a call centre. The events must be independent in the sense that the arrival of one call will not make another more or less likely, but the probability per unit time of events is understood to be related to covariates such as time of day.

參考網頁:

http://en.wikipedia.org/wiki/Poisson_regression

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