CONTENTS 1. Introduction.............................................................................................................................................................................................5 2. A theorem for a multinomial population...................................................................................................................................................5 3. Estimation of frequencies of population with a hierarchic structure........................................................................................................7 4. Estimation of frequencies of multinomial population under a general quadratic loss Function..............................................................12 5. A problem of prediction.........................................................................................................................................................................15 6. Minimax estimation of distribution function............................................................................................................................................17 7. Sequential minimax estimation for stochastic processes in the case where there exists a sufficient statistic for the parameter............18 8. Sequential estimation for the multinomial process.................................................................................................................................21 9. Exponential family of processes............................................................................................................................................................23 10. Sequential estimation for a multivariate process.................................................................................................................................24 11. Sequential estimation for the Poisson process....................................................................................................................................29 12. Sequential minimax estimation in the case where the set of a priori distributions of the parameter is restricted.................................31 13. Continuation to Section 12..................................................................................................................................................................37 14. Final remarks......................................................................................................................................................................................40 References...............................................................................................................................................................................................41
Politechnika Wrocławska, Instytut Matematyki, Wrocław, Polska
Institute of Mathematics, Technical University, Wrocław, Poland
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