Poisson distributed data is intrinsically integer-valued, which makes sense for count data. Ordinary Least Squares (OLS, which you call "linear regression") assumes that true values are …

This was in discussions of interpreting logistic regression coefficients, but Poisson regression is similar if you use an offset of time at risk to get rates. You add first all the coefficients …

Mar 29, 2023 · 3 I am trying to perform an apriori power analysis to estimate sample size for a Poisson regression model. The background is that a RCT is proposed to compare the rate of …

When to use robust standard errors in Poisson regression? Ask Question Asked 13 years, 1 month ago Modified 12 years, 3 months ago

A Poisson regression is a regression where the outcome variable consists of non-negative integers, and it is sensible to assume that the variance and mean of the model are the same. …

In Poisson regression, I need to compute the deviance, in order to do that I need to compute the log-likelihood function. It seems not difficult because I have the estimated model and my data …

Dec 7, 2023 · Poisson regression is commonly used to analyse count data. However, when we deal with rare events it does not seem to be appropriate any more. At least, graphical criteria …

Does anybody know why offset in a Poisson regression is used? What do you achieve by this?

From Poisson regression, relative risks can be reported, which some have argued are easier to interpret compared with odds ratios, especially for frequent outcomes, and especially by …

Oct 4, 2015 · the Poisson distribution, named after French mathematician Siméon Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events …