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From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Blown up Std. errors in logistic regression with bootstrap |

Date |
Mon, 13 Dec 2010 10:55:51 -0500 |

A cheat code to get out of that is to bootstrap independently the positive and zero cases with -strata()- option. This will make your results conditional on the total # of outcomes, somewhat in the spirit of -clogit- models. Of course getting more cases would be a much better solution, as essentially you will underestimate your variability by the contribution of the variability in the # of positive outcomes. Think about the total variance formula with some conditional expectations: Var[ beta-hat ] = E[ Var[ beta-hat | # of 1's] ] + Var[ E[ beta-hat| # of 1's ] The bootstrap with fixed # of 1's estimates only the inside variance of the first term. If you are nearing the perfect prediction, the contribution of the second term will arguably be a small component. You can assess their relative magnitudes via predict prob, prob gen var_coutcome = prob*(1-prob) sum var_coutcome prob The variance of -prob- will give the relative contribution of the first term, and mean of -var_coutcome-, the relative contribution of the second term. This derivation assumes that the information contained in the positive outcomes is spread equally among betas, which of course is a pretty strong assumption. On Mon, Dec 13, 2010 at 10:00 AM, Michael Wahman <Michael.Wahman@svet.lu.se> wrote: > Thank you very much for your excellent advice! I really appreciate it. As > you suggested, I used the noisily option to diagnose the problem. It turned > out to be exactly the way you suspected. Most of the models were close to > being perfectly determined. -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: Blown up Std. errors in logistic regression with bootstrap***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**Re: st: Blown up Std. errors in logistic regression with bootstrap***From:*Michael Wahman <Michael.Wahman@svet.lu.se>

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