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st: RE: When to use ML or bootrapping


From   Nick Cox <n.j.cox@durham.ac.uk>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: When to use ML or bootrapping
Date   Tue, 30 Nov 2010 15:10:18 +0000

These aren't really alternatives in any sense. Indeed it is common to combine maximum likelihood (ML) estimation and bootstrapping. 

Often, ML provides estimates and bootstrapping gives (more) honest indications of their uncertainty than you might otherwise get. 

However, you can bootstrap anything you can program, more or less, including procedures in which ML cannot be specified or is not possible for whatever reason. 

Conversely, ML might well be possible in circumstances in which bootstrapping is problematic (e.g. many time series problems). 

The book you refer to doesn't aim to cover likelihood from scratch. You need an intermediate book that covers both in context. I like 

Davison, A.C. 2003. Statistical models. Cambridge U.P. 

Rice, J.A. 2007. Mathematical statistics and data analysis. Duxbury or whatever it's called this year. 

But the choice is very personal, and you might well prefer something with more business or economic examples. 

Nick 
n.j.cox@durham.ac.uk 

Amy Dunbar

Although these questions are not directly Stata related, I hope someone will suggest sources to help me.  I want to know how one knows if you should consider ML or bootstrapping.

I read the preface to Maximum Likelihood Estimation with Stata, Fourth Edition, http://www.stata.com/bookstore/pdf/ml4-preface.pdf
I don't see a chapter on when ML is appropriate, unless that is covered in the practical implications discussion.

As for bootstrapping, Chapter 13 in Cameron and Trivedi helped me understand how to use bootstrapping, but not when I should consider using bootstrapping.

I apologize for my newbie questions, but I would really appreciate some intuition.

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