Stochastic Models, Statistics and Their Applications : Dresden, Germany, March 2019. 1st ed. 2019

種類:

電子ブック

責任表示:

edited by Ansgar Steland, Ewaryst Rafajłowicz, Ostap Okhrin

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2019

著者名:

• Steland, Ansgar.
• Rafajłowicz, Ewaryst.
• Okhrin, Ostap.
• SpringerLink (Online service)

シリーズ名:

Springer Proceedings in Mathematics & Statistics ; 294

ISBN:

9783030286651 [3030286657]    

注記:

PART 1: Plenary Lectures: W. Stute, Stairway to hell -- I. Gijbels, R. Karim, A. Verhasselt, Quantile estimation in a generalized asymmetric distributional setting -- M. Ljungdahl, M. Podolskij, A note on parametric estimation of Levy moving average processes -- PART 2: Theory and Related Topics: K. Knight, A continuous-time iteratively reweighted least squares algorithm for $L_¥infty$ estimation -- M. Bibinger, M. Trabs, On central limit theorems for power variations of the solution to the stochastic heat equation -- P. Gapeev, Perpetual dual American barrier options for short sellers -- P. Lachout, A criterion for weak convergence in vector Skorokhod spaces -- E. Liebscher, On combining star-shaped distributions and copulas -- E. Skubalska-Rafajlowicz, Stability of the Random-Projection Based Classifiers. The Bayes error perspective -- A. Ishii, K. Yata, M. Aoshima, A quadratic classifier for high-dimension, low-sample-size data under the strongly spiked eigenvalue model -- Z. Hlávka, M. Huskova, Doubly pai
This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science,

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