Teaching Decision Theory Proof Strategies Using a Crowdsourcing Problem

Autor(es)

Esteves, Luis Gustavo ^[1] ; Izbicki, Rafael ^[2] ; Stern, Rafael Bassi ^[2]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Dept Stat, Sao Paulo - Brazil

^[2] Univ Fed Sao Carlos, Dept Stat, Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: AMERICAN STATISTICIAN; v. 71, n. 4, p. 336-343, 2017.
• Citações Web of Science: 1

Resumo

Teaching how to derive minimax decision rules can be challenging because of the lack of examples that are simple enough to be used in the classroom. Motivated by this challenge, we provide a new example that illustrates the use of standard techniques in the derivation of optimal decision rules under the Bayes and minimax approaches. We discuss how to predict the value of an unknown quantity,theta.epsilon [0, 1], given the opinions of n experts. An important example of such crowdsourcing problem occurs in modern cosmology, where theta indicates whether a given galaxy is merging or not, and Y-1,..., Y-n are the opinions from n astronomers regarding theta We use the obtained prediction rules to discuss advantages and disadvantages of the Bayes andminimax approaches to decision theory. The material presented here is intended to be taught to first- year graduate students. (AU)

• Processo FAPESP: 17/03363-8 - Interpretabilidade e eficiência em testes de hipótese
• Beneficiário: Rafael Izbicki
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/25302-2 - Uma abordagem flexível para a estimação de uma densidade condicional em problemas com alta dimensionalidade
• Beneficiário: Rafael Izbicki
• Modalidade de apoio: Auxílio à Pesquisa - Regular