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Quant analytics: Is it possible to improve a predictive model using the data that come after the model is applied?

(Last Updated On: October 13, 2011)

Quant analytics: Is it possible to improve a predictive model using the data that come after the model is applied?

The new data are supposed to be biased towards the treatment. So how to utilize them in this case? Thanks.

That depends on the model in question. Your initial question is, unfortunately, too general to give a specific answer. You should possibly look into Bayesian Inference or hierarchial Bayesian modelling, with your initial model as your prior.

 

Yes, consider a Self Organizing Map (SOM). This separates your variables and allows you to find those that have higher correlations between the other variables. “After this model is applied,” you can formulate weights based on these results into a stronger model such as a neural network.

 

Thank you for the comments. I will look into Bayesian modelling and neural network. Sorry for too general a question. Let’s say, for example, a logistic regression model predicts the survival rate based on a set of variables. One variable is identified as significant and some work is then done to make improvement on it. After a while, new data are accumulated. So what to do with the new data? Thanks a lot.

 

which model is good one with higher efficiency or vice -versa is their any criteria to choose best one

 

You might need to search for cost-sensitive learning or even online/incremental cost-sensitive learning. In this type of learning, you assign different costs to different classes. Online or incremental learning allows you to update a model as new data arrives without accessing old data. Just search with Google/GoogleScholar for “online cost-sensitive learning” or just “cost-sensitive learning”.

 

 

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