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From |
"Naji Nassar \(MIReS\)" <naji.nassar@mires.fr> |

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

Subject |
st: RE: Signficance vs prediction |

Date |
Wed, 10 Mar 2004 13:35:29 +0100 |

David, I'm used to compare between strikes getting better RMSE and costs of variables (no costs for cognition) Ideas - Test both models on data which have'nt beeb used for model estimation ..> RMSE - Robust regression : excluding some extreme values (can it be explained) - Absolute deviance rather than squares.. Best Naji -----Message d'origine----- De : owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu]De la part de David Vaughan Envoye : mercredi 10 mars 2004 08:11 A : statalist@hsphsun2.harvard.edu Objet : st: Signficance vs prediction I know this is pretty simple but the answer is not obvious in my old texts and in business I have no expert colleague to whom to turn. My purpose is to construct a model which will be used for best-possible prediction from new input data. I constructed a regression model, based on historical understanding of the domain, using eight predictors and obtained the following data about the model: F(8,98) = 35.15 Adj R-squared = 0.7205 RMSE = 0.90373 I noted that three of the predictors had P>| t | around 0.2-0.24. Eliminating those gave me model results: F(5,101) = 54.49 Adj R2 = 0.7295 RMSE = 0.91067 So significance has gone up but so has error. I assume that the larger model over-fits the data and, if I were arguing around causaility, would prefer the more compact model. Yet, it seems that the larger model just does a slightly better job of prediction. How do I think about this? Generally, where do I stop in a predictive problem (there are other inputs available)? Should I care that much about a minor RMSE difference or just do a "judgement" check on error differences on new data? I also did a decent (N=1000) bootstrap on the larger model and confidence intervals around all the predictors appeared reasonable for our purpose. Either of the above models serves better than our previous approach although it seems (opinion) that the larger model does better at the extremes. Talking to myself, I wonder if I just need more data for analysis (painful process) but is there a statistical approach to focussing on that extreme-edge issue? Perhaps I should be looking for another inflection point in the model - we have already found one at the other end, which I omitted from the above for brevity. If so, how does one find it other than by trial? Any advice or reading directions welcome. thanks David * * 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**:**st: Signficance vs prediction***From:*David Vaughan <dvk@dvkconsult.com.au>

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