Tweedie’s Formula and Selection Bias

by Bradley Efron

Datum	Value	Source
TITLE	Tweedie’s Formula and Selection Bias	SVM HeaderParse 0.2
AUTHOR NAME	Bradley Efron	SVM HeaderParse 0.2
AUTHOR AFFIL	Stanford University	SVM HeaderParse 0.2
ABSTRACT	We suppose that the statistician observes some large number of estimates zi, each with its own unobserved expectation parameter µi. The largest few of the zi’s are likely to substantially overestimate their corresponding µi’s, this being an example of selection bias, or regression to the mean. Tweedie’s formula, first reported by Robbins in 1956, offers a simple empirical Bayes approach for correcting selection bias. This paper investigates its merits and limitations. In addition to the methodology, Tweedie’s formula raises more general questions concerning empirical Bayes theory, discussed here as “relevance ” and “empirical Bayes information. ” There is a close connection between applications of the formula and James–Stein estimation. Keywords: Bayesian relevance, empirical Bayes information, James–Stein, false discovery rates, regret, winner’s curse	SVM HeaderParse 0.2
CITATIONS	24 found	ParsCit 1.0