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I am building a predictive model using logistic regression to predict if an applicant should be given a credit product based on their telecommunication data of the previous eight months postpaid subscribers only. But the behaviors I am trying to capture can vary with time for the entire population. Example: data usage, spending power, etc will vary with time. So the question is how to build a model that performs consistently across time. Else, a suggestion on how to refresh/update the model with time should help as well.

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  • $\begingroup$ Interesting question. It would help to give a little more detail about the predictors involved. Some may be extensive, some intensive with respect to time (Intensive and extensive properties). If I understand right, the issue with, say, data usage is that an aggregate measure over a short interval is subject to fluctuations that convey little or no information about someone's suitability for the credit product, whereas an aggregate measure over a long interval includes information that may no longer be relevant. $\endgroup$ – Scortchi - Reinstate Monica Apr 28 '16 at 9:58
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Every time you conduct the analysis, you could use the latest obtained data points, for instance, data collected in the last 180 days. I suppose the applicants' data is stored and updated on database maintained by you or a third party. So a script would help to do the routine work, i.e., data manipulation, etc.

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    $\begingroup$ Alternatively you can fit time as a flexible nonlinear effect and include all the data. $\endgroup$ – Frank Harrell Apr 28 '16 at 11:37
  • $\begingroup$ @FrankHarrell: Thanks Frank. Your suggestion sounds like a good one. Could you give more pointers on how to do this? I tried searching on the internet but didn't get much. A link/pdf or any resource/example would help $\endgroup$ – Clock Slave May 2 '16 at 9:35
  • $\begingroup$ Regression splines are described in my course notes at biostat.mc.vanderbilt.edu/rms $\endgroup$ – Frank Harrell May 2 '16 at 12:44

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