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R: st: Tobit regression Model


From   "Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>
To   <statalist@hsphsun2.harvard.edu>
Subject   R: st: Tobit regression Model
Date   Sun, 2 Dec 2012 19:51:25 +0100

I agree with Jay about the trade-off between research assumptions and
empirical evidence.
About Jay's second point, my smattering of the Bayesian framework is limited
and, at least in Italy, it is not common at all. I usually go (empirical)
Bayesian for running probabilistic sensitivity analysis (I'm a health
economist)  and play on the safe side of the matter with conjugated
distributions. Others may find hard times in dealing with more intimidating
facets of Rev Bayes' machinery (eg: improper priors)!

Best wishes,
Carlo

-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di JVerkuilen
(Gmail)
Inviato: domenica 2 dicembre 2012 18:44
A: statalist@hsphsun2.harvard.edu
Oggetto: Re: st: Tobit regression Model

On Sun, Dec 2, 2012 at 12:00 PM, Carlo Lazzaro <carlo.lazzaro@tiscalinet.it>
wrote:
> Dear Jay,
> thanks for the plug.
> Yes, I have heard something alike to what you report about handling 
> with care Tobit model.

Tobit depends on normality quite strongly in a way that neither of its
parent models, ordinary linear regression or probit regression, do.


> Erring on the high side of N is always a good practice. Unfortunately, 
> this is often a matter of (tight) research funds.

Well it is, absolutely, but if you don't have the dataset you want you
either trade off assumptions (e.g., Bayesian estimation with informative
priors, not an easy thing to do) or run a simpler model than you would like.
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