[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu> |

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

Subject |
RE: st: problem with MLE |

Date |
Mon, 3 Dec 2007 13:06:19 -0500 |

Maarten Buis wrote: >>I can't spot the problem, but I can tell you that a useful debugging strategy is to start with a much smaller program and gradually build it to the full model that is desired. Substantively such smaller models may be completely unacceptable within your discipline, but don't worry about that, it is just a step in getting the mechanics to work. Sometimes such smaller models happen to coincide with models that are already implemented in Stata, so you can use that as a check of whether you start from the right point.<< I think Maarten's advice is right on. Build your models sequentially starting from simple but probably wrong ones that are constrained versions of the one you want to estimated. The numerical methods used in ML estimation are all "locally convergent" which means that unless they are started sufficiently close to the right answer (or unless certain global optimality conditions are met, which is not true generally), they can do all sorts of bad things: diverge (the least bad because you know you don't have an answer), converge to a local optimum that looks like a good solution (bad!), throw an improper solution (you'll see standard errors blow up, usually), etc. Sometimes you have to play with the numerical functions, e.g., switch from a quasi-Newton to a full Newton or use a different type of numerical integration, to make progress. In fact, if you look at the way Stata initializes complex models like, say, ZINB (zero-inflated negative binomial), it does exactly this, first fitting the Poisson model, then fitting the NB model with no regressors, etc. This adds estimation time but always gives the next model better starting values from which to work. I've fit probably hundreds of mixed ML models in various packages (Stata, SAS, R, winBUGS, homegrown, etc.). All have peculiarities about how they handle the initialization process, how robust they are to problems, what their error codes *actually* mean, which program bugs haven't been fixed yet. The experience you gain fitting the simpler models that don't entirely line up with your problem is invaluable for finding out what is likely to go wrong when you move up to the next level. It's not a question of if, but when, things go wrong.... Jay -- J. Verkuilen Assistant Professor of Educational Psychology City University of New York-Graduate Center 365 Fifth Ave. New York, NY 10016 Email: jverkuilen@gc.cuny.edu Office: (212) 817-8286 FAX: (212) 817-1516 Cell: (217) 390-4609 * * 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**:**Re: st: problem with MLE***From:*Maarten buis <maartenbuis@yahoo.co.uk>

- Prev by Date:
**RE: st: Quantal Response Equilibrium with Stata** - Next by Date:
**Re: st: variance when using svy: mean** - Previous by thread:
**Re: st: problem with MLE** - Next by thread:
**st: Lowess regresion** - Index(es):

© Copyright 1996–2022 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |