Here I share some things I find particularly interesting, inspiring, or worth reading.
Some papers have stayed with me because they changed the way I approach a problem, introduced me to a perspective I had not previously considered, or made something I had struggled to understand suddenly seem clear. Here I collect some of those papers. The list is neither exhaustive nor intended as an assessment of their objective importance; it simply reflects what I have personally found intellectually valuable.
Rohit K. Patra, Bodhisattva Sen, and Gábor J. Székely (2016)
Statistics & Probability Letters, 109, 208–213.
Why I like it: This paper introduced me to a unified notion of an error term that does not require assumptions of linearity, additive errors, or a particular functional form. It was a major inspiration for the fourth section of my master's thesis, and it continues to influence how I think about regression models and structural equations.
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Ben O'Neill (2009)
International Statistical Review, 77(2), 241–250.
Why I like it: This paper helped me understand an interesting Bayesian interpretation of the correlation between observations in a predictive distribution. It also helped me distinguish between epistemic and causal independence.
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