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From |
Woolton Lee <finished07@gmail.com> |

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

Subject |
st: question about error in ML maximization |

Date |
Fri, 10 Sep 2010 17:02:06 -0400 |

Dear Statalist, I am running a program that estimates a hospital choice model using patient discharge data then bootstraps the data 100 times using sample sizes equal to the original dataset. The first step is used to compute a mean effect, then the bootstrapping is used to calculate standard errors for the estimate of the mean. Whenever I estimate the model using the unbootstrapped data there is no problem with convergence. However, whenever the model is being estimated on the bootstrapped dataset (usually after about 50-90 bootstraps), I encounter the error, "discontinuous region encountered, cannot compute an improvement r(430)". I would like to better understand what this error means and also what types of data problems could cause it. Because this error only occurs when I am bootstrapping, which by the way is clustered on patient zip code and stratified by year and market, it seems that one possible cause is that the bootstrapped samples may contain little variation causing the model to crash after several iterations. Could someone please comment on what this error means and provide some examples of what could cause it? When bootstrapping, the mean from each iteration is stored in a vector (which is later used to compute a standard error), and since the model typically crashes after completing 50 or more iterations I am thinking of saving the completed iterates and combining them from another run with 50 or more iterates to obtain 100 bootstrapped means for computing the standard errors. Could someone also comment on the statistical validity of calculating standard errors by pooling separate runs of the program? I would like to get some reactions to what I am thinking of doing. Thanks for your help, Wtn * * 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/

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