Are likelihood approaches frequentist?

Thanks, Rub?n

My point with this topic was to clarify that the likelihood-based 
approach is a distinct paradigm in statistical inference, and there is 
people in biology applying it successfully.

It's a very good point to make. Another important paper is Rubin's paper 
on missing data: Biometrika 63:581-592, 1976. There Rubin basically 
shows that it is easier to make statistical models for data with 
Bayesian and likelihoodist inference, because the mechanism generating 
missing data can be ignored if the missing data is missing at random, 
whereas in sampling distribution inference this conditions is not 
sufficient. To ignore the mechanism generating missing data it is also 
necessary that the observed data be observed at random. Many usual 
scientific studies involve missing data, such s random sampling from 
finite populations, randomized experimental set up, etc.

I agree with you that this point should be better stressed, specially 
for biologists. Taper & Lelle  "The Nature of Scientific Evidence" 
(Chigago Univ Press, 2007) is a great help in this respect.

Thanks for this reference. I've missed it.

Could you indicate the best works by Nelder Lindsey that could 
contribute to this point?

In the case of Nelder, I only know of his personal statement in the 
quote that I gave by mistake in my first post, complemented in the 
second post.
Lindsey has a very interesting paper in The Statistician (apart from 
heresies): Relationship between sample size, model selection and 
likelihood regions, and scientifically important differences. The 
Statistician 48:401-411.
Regards
Rub?n