Recently, my classmate asked me how to perform empirical Bayesian shrinkage, a form of estimation that tries to adjust your sample mean to the grand mean by incorporating more variables. I haven't done this as part of my regular work so I had to review my past class notes.
I forgot how useful empirical Bayes estimates were and wanted to document what I discovered. In my research, I discovered an informative guide by David Robinson who used baseball statistics as a motivating example to explain empirical Bayesian shrinkage on his blog.
In addition, Nicolas Lartillot wrote a great summary of empirical Bayes estimation and Stein's paradox on his blog.