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From |
Garry Anderson <g.anderson@unimelb.edu.au> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: conditional logistic |

Date |
Sat, 27 Oct 2007 08:14:22 +1000 |

Dear Statalist, In the very last paragraph Bill mentioned The logic, "if the number of estimates increases at the same rate as number of observations, there will be problems" is generally true, the exception being cases where there is a particular kind of separability, which happens only in the linear case. I think another exception is the Poisson case. My reason for saying this is 1) Hilbe (2007) Page 202 "However, it has been demonstrated by Greene (2006) and others that the IP problem is not real when applied to the Poisson model. This conclusion is based on the observation that the Poisson conditional fixed-effects estimator is numerically equal to the unconditional estimator, which means that there is no IP problem. On the other hand, the IP problem does affect the unconditional fixed-effects negative binomial." 2) Allison (2005) Page 57 "This is not a problem for linear models and (somewhat surprisingly) for the Poisson models discussed in Chapter 4. But it is a serious problem with logistic regression and many other nonlinear regression models." 3) Allison (2005) Page 90 "...we find the coefficients for the R&D measures and for the time dummies are identical for the conditional and unconditional methods. Standard errors, chi-squares and p-values are also identical for these variables. Unlike logistic regression, for which conditional and unconditional estimates can differ substantially, these two methods always produce identical results for Poisson regression (Cameron and Trivedi 1998)." References: Allison PD (2005) Fixed Effects Regression Methods for Longitudinal Data Using SAS. SAS Press. Cameron AC and Trivedi PK (1998) Regression Analysis of Count Data. Cambridge University Press. Greene WH (2006) LIMDEP Econometric Modeling Guide, Version 9, Plainview, NY: Econometric Software Inc. Hilbe JM (2007) Negative Binomial Regression. Cambridge University Press. Best wishes, Garry Garry Anderson School of Veterinary Science University of Melbourne 250 Princes Highway Ph 03 9731 2221 WERRIBEE 3030 Fax 03 9731 2388 Email: g.anderson@unimelb.edu.au -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of William Gould, StataCorp LP Sent: Friday, October 26, 2007 12:56 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: conditional logistic Ricardo Ovaldia <ovaldia@yahoo.com> asks, > What is the difference between conditional logistic regression > grouping on clinic and unconditional logistic regression including > clinic as a dummy > (indicator) variable? That is, what is the difference in model > assumptions and parameter estimates? The difference is that the logistic regression estimates are inconsistent and bad. --cut-- The logic, "if the number of estimates increases at the same rate as number of observations, there will be problems" is generally true, the exception being cases where there is a particular kind of separability, which happens only in the linear case. <end> * * 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/ * * 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: conditional logistic***From:*wgould@stata.com (William Gould, StataCorp LP)

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