UC Santa Barbara
Predictive influence of unavailable values of future explanatory variables in a linear model
- Author(s): Bhattacharjee, SK
- Shamiri, A
- Sabiruzzaman, M
- Jammalamadaka, SR
- et al.
Published Web Locationhttps://doi.org/10.1080/03610926.2010.513794
We consider an approach to prediction in linear model when values of the future explanatory variables are unavailable, we predict a future response y f at a future sample point x f when some components of x f are unavailable. We consider both the cases where x f are dependent and independent but normally distributed. A Taylor expansion is used to derive an approximation to the predictive density, and the influence of missing future explanatory variables (the loss or discrepancy) is assessed using the Kullback-Leibler measure of divergence. This discrepancy is compared in different scenarios including the situation where the missing variables are dropped entirely. © 2011 Copyright Taylor and Francis Group, LLC.