Asymptotic Statistics

1. Front Matter

   Pages I-X

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2. Invited Papers

   1. Front Matter

      Pages 1-1

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   2. Procedures for the Detection of Multiple Changes in Series of Independent Observations

      • Jaromír Antoch, Marie Hušková

      Pages 3-20

   3. Probing for Information in Two-Stage Stochastic Programming and the Associated Consistency

      • Zvi Artstein

      Pages 21-33

   4. Outliers and Switches in Time Series

      • A. C. Atkinson, Siem Jan Koopman, Neil Shephard

      Pages 35-48

   5. Fréchet Differentiability and Robust Estimation

      • Tadeusz Bednarski

      Pages 49-58

   6. Stein Predictors and Prediction Regions

      • Rudolf Beran

      Pages 59-74

   7. Perpetuities and Random Equations

      • Paul Embrechts, Charles Goldie

      Pages 75-86

   8. On Recent Developments in the Theory of Set-Indexed Processes

      • Peter Gaenssler

      Pages 87-109

   9. Regression Rank Scores Scale Statistics and Studentization in Linear Models

      • Jana Jurečková, Pranab K. Sen

      Pages 111-121

   10. On an Argmax-Distribution Connected to the Poisson Process

       • Georg Ch. Pflug

       Pages 123-129

   11. Asymptotic Normality of Regression M-Estimators: Hadamard Differentiability Approaches

       • Jian-Jian Ren, Pranab Kumar Sen

       Pages 131-147

3. Contributed Papers

   1. Front Matter

      Pages 149-149

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   2. Asymptotic Theory for Regression Quantile Estimators in the Heteroscedastic Regression Model

      • Marc Aerts, Paul Janssen, NoëL Veraverbeke

      Pages 151-161

   3. Nonnegative Moving-Average Models

      • Jiří Anděl

      Pages 163-171

   4. Biased Samples from a Search and the Asymptotic Behaviour of Bayesian Models

      • J. A. Bather

      Pages 173-182

   5. A Modification of Least Squares with High Efficiency and High Breakdown Point in Linear Regression

      • Konrad Behnen

      Pages 183-194

   6. Significance of Differences of Estimates

      • Pavel Boček, Jan Ămos Víšek

      Pages 195-202

   7. Adaptiveness in Time Series Models

      • Feike C. Drost, Chris A. J. Klaassen, Bas J. M. Werker

      Pages 203-211

   8. Kernel Estimators of Integrated Squared Density Derivatives in Non-Smooth Cases

      • A. J. van Es, A. J. Hoogstrate

      Pages 213-224

The Prague Symposia on Asymptotic Statistics represent a twenty years' tradi­ tion of contacts between Czech mathematical statisticians and the conference partic­ ipants. Both, as the organizers hope, return from the Symposia to their work with fresh ideas and new information. The Fifth Prague Symposium was held from September 4 to September 9,1993 at the Faculty of Mathematics and Physics, Charles University. It was sponsored by the Bernoulli Society for Mathematical Statistics and Probability, the Czech Statistical the Czech Society of Actuaries, Ceska Pojistovna-Insurance and Reinsur­ Society, ance Corporation, and the IFIP WG 7.7. Asymptotic Statistics, a prolific source of methodological concepts, dominated the program of the Symposium. Special sessions were devoted to Mathematics of Insurance and Finance and to Stochastic Programming. The papers presented at the Symposium are published in two parts. Part 1 is .. Part 2 is Number 3, Volume 30 (1994) of the journal Kybernetika, this volume comprising the papers of the authors who were not able to meet the early editorial deadline. The editors of the Proceedings would like to express their sincere thanks to the authors for valuable contributions, to the reviewers for prompt and careful reading the papers, to J. Antoch for his advice with technical part of the Proceedings. Finally they also express their appreciation of the kind cooperation with the Publishing House Physica-Verlag and the journal Kybernetika in bringing out the volumes. Part of the Proceedings was typeset by AN(S-TEX, the macrosystem of the Ame­ rican Mathematical Society.

• Asymptotic Statistics
• Asymptotische Statistik
• Bootstrap
• Estimator
• Likelihood
• Probability distribution
• Random variable
• Regression analysis
• Robust Statistics
• Robuste Statistik
• Time series
• Variance
• linear regression

• Department of Probability and Mathematical Statistics, Charles University, Praha 8, Czech Republic

  Petr Mandl, Marie Hušková

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