Γ-minimax sequential estimation for Markov-additive processes

• Autorzy: Ryszard Magiera
• Języki publikacji: EN

Abstrakty

EN

The problem of estimating unknown parameters of Markov-additive processes from data observed up to a random stopping time is considered. To the problem of estimation, the intermediate approach between the Bayes and the minimax principle is applied in which it is assumed that a vague prior information on the distribution of the unknown parameters is available. The loss in estimating is assumed to consist of the error of estimation (defined by a weighted squared loss function) as well as a cost of observing the process up to a stopping time. Several classes of optimal sequential procedures are obtained explicitly in the case when the available information on the prior distribution is restricted to a set Γ which is determined by certain moment-type conditions imposed on the prior distributions.

Kategorie tematyczne

• 62C20: Minimax procedures
• 62L12: Sequential estimation
• 62F15: Bayesian inference

• Wydawca: Institute of Mathematics Polish Academy of Sciences
• Czasopismo: Applicationes Mathematicae
• Rocznik: 2001
• Tom: 28
• Numer: 4
• Strony: 467-485

Daty

wydano

2001

Twórcy

autor

Ryszard Magiera

• Institute of Mathematics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Identyfikatory

DOI

10.4064/am28-4-7