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E-Book, Englisch, Band 208, 511 Seiten

Reihe: Springer Proceedings in Mathematics & Statistics

Panov Modern Problems of Stochastic Analysis and Statistics

Selected Contributions In Honor of Valentin Konakov
1. Auflage 2017
ISBN: 978-3-319-65313-6
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

Selected Contributions In Honor of Valentin Konakov

E-Book, Englisch, Band 208, 511 Seiten

Reihe: Springer Proceedings in Mathematics & Statistics

ISBN: 978-3-319-65313-6
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark



This book brings together the latest findings in the area of stochastic analysis and statistics. The individual chapters cover a wide range of topics from limit theorems, Markov processes, nonparametric methods, acturial science, population dynamics, and many others. The volume is dedicated to Valentin Konakov, head of the International Laboratory of Stochastic Analysis and its Applications on the occasion of his 70th birthday. Contributions were prepared by the participants of the international conference of the international conference 'Modern problems of stochastic analysis and statistics', held at the Higher School of Economics in Moscow from May 29 - June 2, 2016. It offers a valuable reference resource for researchers and graduate students interested in modern stochastics.

Vladimir Panov is an assistant professor at the National Research University - Higher School of Economics (Moscow).  He graduated from the Moscow State University, and completed his PhD at the Humboldt University in Berlin. During his PhD and postdoctoral studies, he worked on various projects within the Collaborative Research Centres (SFB) on economic risks and statistical modelling of nonlinear dynamic processes.  He was a research assistant at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin (2008-2011), and later a postdoc at the University of Duisburg-Essen (2011-2013). His current research interests are primarily in the field of statistical inference for stochastic processes and Levy-based models.

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