Over the next few months, I will be reexamining some old surplus modeling data from the early 1990’s and using some of the new predictive analytics that have been in the press lately.
I have also have located an excellent article that discusses the issue between explanation and prediction.
Dr. Shmeli’s article describes the difference between these two in statistical modeling. He discusses the research areas that have nuanced issues around the topics as well.
In the future blogs and studies, I will be using the term predictive analytics, but my primary goal will be to use those techniques to explain or lead to new insights. Only when I discuss using these models as possible dashboard, will I be using the stochastic results to create possible predictive tools.
My primary research in the past has been to determine how I can extract as much information as possible from using stochastic modeling. This is because, in the past, the cost of the overhead with stochastic runs was prohibitive. I would use various tools from either fitting distributions, quantile regression or from extreme value theory to extract additional information from those results.
Using distribution fitting, I built several models that would replicate the overall support of a stochastic model, without the cost of the actual simulation. However, I found that there was no parametric distribution that could actually fully replicate the results of a stochastic model.
In the next post, I’ll share some of those results to give you insight into my filter that I will be using as I move forward with the new modeling techniques.