Before revision:  In a linear regression model with homoscedastic Normal noise, I consider James-Stein type shrinkage in the estimation of nuisance parameters associated with control variables.
After revision: Shrinkage estimation usually reduces variance at the cost of bias. But when we care only about some parameters of a model, I show that we can reduce variance without incurring bias if we have additional information about the distribution of covariates. In a linear regression model with homoscedastic Normal noise, I consider James-Stein type shrinkage in the estimation of nuisance parameters associated with control variables.