This paper considers linear model selection when the response is vectorvalued and the predictors, either all or some, are randomly observed. We propose a new approach that decouples statistical inference from the selection step in a "post-inference model summarization" strategy. We study the impact of predictor uncertainty on the model selection procedure. The method is demonstrated through an application to asset pricing.
- Decoupling shrinkage and selection
- Penalized utility selection
- Seemingly unrelated regressions
ASJC Scopus subject areas
- Statistics and Probability
- Applied Mathematics