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When is it appropriate to apply Firth Adjusted Maximum Likelihood and FDR?

Currently running a Poisson model in the Generalized Linear Model personality. The personality provides the opportunity to provide Firth Bias-Adjusted Estimates. Additionally, once the model has been run, I have the option to apply a False Discovery Rate to each model effect. Just was curious...when should I use these options? 


Re: When is it appropriate to apply Firth Adjusted Maximum Likelihood and FDR?

First of all, see Help > Books > Fitting Linear Models. There is a lot of information about fitting and interpreting the GLMs.


The Firth bias adjustment has two main purposes. The first purpose is to decrease bias and standard error of the parameter estimate by shrinking the estimate towards zero. This problem is a concern with small data sets or where a predictor is associated with one level in the case of a binomial GLM (e.g., logistic regression). The second purpose of the method is to solve the problem or complete or quasi-separate in the case of logistic regression.


The FDR is useful when you have very many simultaneous t-tests (i.e., significant estimate).

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