The Foundations of Statistics
By D. Lindley, L. J. Savage. 1955
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Mohammed Abdellaoui
- Han Bleichrodt
- Gerd Gigerenzer
- Christopher A. Fuchs
- Larry Samuelson
- Daron Acemoglu
- Antony Millner
- Adam S. Booij
Citations in the corpus
- Proper local scoring rules (2011, background): “The principles of Bayesian decision theory [Savage (1954)] now enjoin You to minimize Your expected loss.”
- The Treynor Capital Asset Pricing Model (2003, background): “The author wishes to thank: Jack and BetsyTreynor, for clarification of numerous facts, as well as for generously providing original copies of Treynor (1961, 1963); William Sharpe, who graciously sent relevant portions of Sharpe (1961) and provided important feedback; Mark Rubinstein, for inspiring ”
- Social influences on risk attitudes: Applications in economics (2012, unknown): “In this case, the decision maker evaluates a prospect by using her own, subjective estimates of the relevant probabilities (Anscombe and Aumann 1963; Savage 1954).”
- Shaping the next one hundred years (2003, methodology): “Following Savage (1950), the regret of strategy j in future f using values m is given as”
- The Impact of Regret on the Demand for Insurance (2004, unknown): “…that are consistent with the Regret Theory axioms of Sugden (1993) and Quiggin (1994) (i.e., preferences that are " regret-theoretical " ) should lead to insurance decisions that are " less extreme " than those that would be derived from preferences consistent with the EUT axioms of Savage (1954).”
- Econometric applications of maxmin expected utility (2000, background): “…g iff min Q2S Z u f dQ min Q2S Z u g dQ: If the certainty-independence axiom is strengthened so that it holds not just for the constant functions but for all r in L, then we have the Anscombe and Aumann (1963) version of the Savage (1954) axioms, and the set S consists of a single distribution.”
- Concurrent Hierarchical Reinforcement Learning (2005, background): “We take a decision-theoretic viewpoint [Savage, 1972], and assume the agent’s uncertainty about the world is represented by a probability distribution.”