I'm interested in exploring statistical models (or modifications thereof) designed to handle a specific type of problem. Due to my ignorance of statistical terminology, I can only describe this type of problem by (contrived) examples:
Suppose we're interested in estimating the likelihood that a given cell phone customer will drop his/her service (churn) over the course of the next month (October). We are given cell phone user data from the last three months (July, August, September).
In estimating churn probability for October, we'll want to make use of a model that emphasizes inputs that are consistently predictive of churn each month, suppressing the importance of those whose coefficients vary heavily on a month-by-month basis. For example, "market" may be consistently predictive of churn, but an externality specific to one of the training datasets (e.g., marketing of new phone by a competitor in a specific market in August) may affect a coefficient in a way that won't necessarily apply to October. The market coefficient should be corrected/compressed/averaged when predicting October's churn likelihood.
Here's a less contrived example. In estimating the likelihood that a voter will vote in the 2012 election, we may use voter-level data from the 2000, 2004, and 2008 elections to train a logistic regression on turnout. If "party" is an input, 2000 and 2004 coefficients may differ significantly from 2008 party coefficient due to exogenous circumstances (a unique political environment). In estimating 2012 vote likelihood for a given voter, we'll want to trim/compress/average the party coefficient to "smooth out" election-year-specific differences that can't be easily quantified.
Can anyone point me in the right direction? Any guidance on how to better ask this question? What terminology should I be using? Thanks in advance for your help.