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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: RE: Testing nested models using logistic regression with robust standard errors |

Date |
Tue, 29 Apr 2008 14:28:20 +0100 |

Well, I agree strongly and I don't. Assessing parsimony and goodness of fit -- which often but not always is a trade-off problem -- are I imagine major issues for many if not most people in this list. Wanting to regard the trade-off as a testing problem is not however universal. I am fortunate enough to find myself in fields where the choice of model depends finally almost always on physical or biological criteria rather than some P-value. (Substitute "economic" or whatever for your own analogue.) One of the most intriguing divides in statistical science is between those whose ideal is evidently a formal set of rules which will guide one ineluctably towards the correct model or decision for any dataset -- and those who doubt very much whether that is desirable, let alone possible. (A pretty large class of questions on this list ends with the question "Is this correct?". Most of those seem to be "econometrically correct".) Those who agree with the first position then commit themselves to a career-long argument with each other about quite what those rules are going to be. Nick Cox John LeBlanc Thanks for the reply. I take your point about the limitations of sw regression and I will be more hesitant in using them. However, whether one uses sw or whether a more appropriate theory-driven approach with thoughtful removal of variables, there is still a problem of testing whether a more parsimonious model differs in the fit of the data from its more saturated model. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: RE: Testing nested models using logistic regression with robust standard errors***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**Re: st: RE: Testing nested models using logistic regression with robust standard errors***From:*John LeBlanc <leblancj@dal.ca>

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