I noticed the blog of Kevin Brancato. I’ve been enjoying reading the blog entries, especially since Kevin is a former student of ours at Columbia! His paper on macroeconomic statistics is also interesting (and relevant to some of my work).

Kevin worked as a research assistant for me a few years ago on a project which eventually appeared in the Journal of Business and Economic Statistics under the title, “Regression Modeling and Meta-Analysis for Decision Making: A Cost-Benefit Analysis of Incentives in Telephone Surveys.”

Here’s the abstract of the paper:

Regression models are often used, explicitly or implicitly, for decision making. However, the choices

made in setting up the models (e.g., inclusion of predictors based on statistical significance) do not

map directly into decision procedures. Bayesian inference works more naturally with decision analysis

but presents problems in practice when noninformative prior distributions are used with sparse data.

We do not attempt to provide a general solution to this problem, but rather present an application of a

decision problem in which inferences from a regression model are used to estimate costs and benefits.

Our example is a reanalysis of a recent meta-analysis of incentives for reducing survey nonresponse.

We then apply the results of our fitted model to the New York City Social Indicators Survey, a biennial

telephone survey with a high nonresponse rate. We consider the balance of estimated costs, cost savings,

and response rate for different choices of incentives. The explicit analysis of the decision problem

reveals the importance of interactions in the fitted regression model.

It was our attempt to perform a full decision analysis, rather than simply looking at some regression coefficients.

P.S. Yes, the tables should be graphs. Especially Table 2.

P.P.S. I don’t know what’s up with Hailin Lou.

P.P.P.S. As a former resident of the D.C. suburbs, I don’t share Kevin’s enthusiasm for a plan to add lanes to the Beltway.