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

From |
"Kok Jb (OS)" <Jb.Kok@os.unimaas.nl> |

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

Subject |
st: maximum likelihood estimation weibull model with sample selection |

Date |
Mon, 14 Feb 2005 17:10:52 +0100 |

Hello, I am trying to model a weibull model with sample selection in stata 8.2. My problem is that Stata produces unsatisfactory output. I have the feeling that rounding in the maximization routines contribute to these results but I am not sure. I provide the output from my do-file. sysuse cancer, clear (Patient Survival in Drug Trial) . . gen drug2 = drug == 2 . gen drug3 = drug == 3 . . . /* > WEIBULL MODEL WITH SAMPLE SELECTION FROM RIGHT TRUNCATED SPELL DATA (OUTFLOW SAMPLE) > > The likelihood for this model is given by > > (1) L = f(t) / F(t) (control for a overrepresentation of short spells relative to long spells) > > with weibull density function (see Maximum Likelihood Estimation with Stata (2nd edition), 2004: page 221) > > f(t; e, g) = (e/g) *(t/e)^(g-1) * exp(-(t/e)^g > > and failure function > > F(t; e, g) = 1 - exp(-t/e)^g > > with parameters (gamma) g and (eta) e=exp(Xb) > */ . . capture program drop myweibull_sampleselection_lf . . program myweibull_sampleselection_lf 1. version 8.1 2. args lnf leta lgam 3. tempvar p M R 4. quietly { 5. gen double `p' = exp(`lgam') 6. gen double `M' = ($ML_y1*exp(-`leta'))^`p' 7. gen double `R' = ln($ML_y1)-`leta' 8. replace `lnf' = -`M' + $ML_y2 * (`lgam' - `leta' + (`p'-1) * `R') - ln(1-(exp(-`M'))) 9. } 10. end . . . ml model lf myweibull_sampleselection_lf (lneta: studytime died = drug2 drug3 age)/ lngamma . . ml maximize initial: log likelihood = -742.8257 alternative: log likelihood = -355.05562 rescale: log likelihood = -192.23867 rescale eq: log likelihood = 452.20041 Iteration 0: log likelihood = 452.20041 (not concave) Iteration 1: log likelihood = 452.31598 (not concave) Iteration 2: log likelihood = 452.35131 (not concave) Iteration 3: log likelihood = 452.35485 Iteration 4: log likelihood = 452.36837 (not concave) Iteration 5: log likelihood = 452.36972 (not concave) Iteration 6: log likelihood = 452.36999 (not concave) numerical derivatives are approximate flat or discontinuous region encountered numerical derivatives are approximate nearby values are missing Iteration 7: log likelihood = 452.37005 (not concave) numerical derivatives are approximate flat or discontinuous region encountered numerical derivatives are approximate flat or discontinuous region encountered Iteration 8: log likelihood = 452.37005 (not concave) numerical derivatives are approximate flat or discontinuous region encountered numerical derivatives are approximate flat or discontinuous region encountered I would really appreciate anyone's ideas about this issue. Best regards, Jasper Kok Ph-D student Maastricht University Netherlands * * 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/

- Prev by Date:
**st: RE: Percent correctly predicted after oprobit** - Next by Date:
**st: one or two factors?** - Previous by thread:
**st: RE: Percent correctly predicted after oprobit** - Next by thread:
**st: one or two factors?** - Index(es):

© Copyright 1996–2016 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |