By Jaromír Antoch, Marie Hušková (auth.), Professor Petr Mandl, Professor Marie Hušková (eds.)

The Prague Symposia on Asymptotic data symbolize a two decades' tradi­ tion of contacts among Czech mathematical statisticians and the convention partic­ ipants. either, because the organizers wish, go back from the Symposia to their paintings with clean rules and new info. The 5th Prague Symposium was once held from September four to September 9,1993 on the college of arithmetic and Physics, Charles college. It used to be backed through the Bernoulli Society for Mathematical facts and likelihood, the Czech Statistical the Czech Society of Actuaries, Ceska Pojistovna-Insurance and Reinsur­ Society, ance company, and the IFIP WG 7.7. Asymptotic facts, a prolific resource of methodological innovations, ruled this system of the Symposium. detailed periods have been dedicated to arithmetic of coverage and Finance and to Stochastic Programming. The papers provided on the Symposium are released in components. half 1 is .. half 2 is quantity three, quantity 30 (1994) of the magazine Kybernetika, this quantity comprising the papers of the authors who weren't in a position to meet the early editorial cut-off date. The editors of the complaints wish to convey their honest because of the authors for useful contributions, to the reviewers for suggested and cautious interpreting the papers, to J. Antoch for his suggestion with technical a part of the court cases. eventually additionally they show their appreciation of the type cooperation with the Publishing residence Physica-Verlag and the magazine Kybernetika in bringing out the volumes. a part of the court cases used to be typeset through AN(S-TEX, the macrosystem of the Ame­ rican Mathematical Society.

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Extra info for Asymptotic Statistics: Proceedings of the Fifth Prague Symposium, held from September 4–9, 1993

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The result of our analysis for any particular shock is a time-series plot accompanied by a simulation envelope. In line with the approach of regression diagnostics, we focus on statistics associated with individual ebservations and specific features of the data or model. 3 Measuring the Effect of Shocks Let the parameter of interest be (J, which is a vector function of the variances in the measurement and transition equations, and let iJ be its maximum likelihood estimator. It is the value of (J which controls the rate of discounting of past observations and so determines the time series properties of the model.

1 ) follows. ) dis- continuous in p. ) is discontinuous. For instance, take h( e) = -1 for -1 :5 e< 0, h(O) = 0 and h(e) = 1 for 0 < e :5 1. Let Pk be concentrated on {i} and Pj concentrated on {=f}. Then both Pk and Pj converge to Po, which concentrate on {OJ, yet EPk(de)(h(e)) = 1, EPj(de)(h(e)) = -1 while EPo(de)(h(e)) = o. 1), it is possible to arrange that x -+ Sz (even x -+ Pz ( de)) be such that the cost is not semi continuous, for instance x E [-1,1] and x = k- 1 is mapped into Pk. ) dicontinuous.

Department of Theoretical Mathematics The Weizmann Institute of Science Rehovot 76100, Israel Outliers and Switches in Time Series A. C. Atkinson*, Siem Jan Koopman* and Neil Shephard t * Department of Statistical and Mathematical Sciences London School of Economics, London WC2A 2AE, UK and tNuffield College, Oxford, OXI INF, UK Abstract For unobserved components or structural time series models, the effect of elaborations of the model on inferences can be investigated by the use of interventions involving a single parameter, such as trend or level changes.

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