2013年10月4日 · Poisson distributed data is intrinsically integer-valued, which makes sense for count data. Ordinary Least Squares (OLS, which you call "linear regression") assumes that …

Poisson regression is typically used to model count data. But, sometimes, it is more relevant to model rates instead of counts. This is relevant when, e.g., individuals are not followed the …

A modified Poisson regression approach to prospective studies with binary data, Am J Epidemiol. 2004 Apr 1;159(7):702-6. Zou G.Y. and Donner A., Extension of the modified Poisson …

Q2: In that case, in a poisson regression, are the exponentiated coefficients also referred to as "odds ratios"? – oort A2: No. If it were logistic regression they would be but in Poisson …

2022年6月17日 · For me it is sort of like a multistable perception where there is a transition between two images that is difficult to see, the conditional probability expression …

The Poisson regression then relates this parameter to the explanatory variables, rather than the count. The reason this is better than normal linear regression is to do with the errors. If our …

2014年5月17日 · Zuur 2013 Beginners Guide to GLM & GLMM suggests validating a Poisson regression by plotting Pearsons residuals against fitted values. Zuur states we shouldn't see …

2022年12月4日 · A natural choice is to use the optimization criterion for calculating the regression parameters. Like other generalized linear models, Poisson regression uses maximum …

The variance of my count data (0.76) is greater than the mean (0.33). I think this indicates that my response variable is "over-dispersed" and that Poisson regression is therefore inappropriate? …

2014年12月14日 · My idea is to do the adjustment by means of Poisson regression. My data is on individual level. I wish to obtain one estimate on the incidence rate (per 1000 person-years) for …