Overview
- Highlights modern analytical methods in statistics and their extensions
- Focuses on estimation, asymptotics, robustness, and stochastics models
- Gathers contributions by experts in the field
Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 329)
Included in the following conference series:
Conference proceedings info: AMISTAT 2019.
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Table of contents (8 papers)
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Front Matter
Other volumes
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Analytical Methods in Statistics
Keywords
About this book
This book collects peer-reviewed contributions on modern statistical methods and topics, stemming from the third workshop on Analytical Methods in Statistics, AMISTAT 2019, held in Liberec, Czech Republic, on September 16-19, 2019. Real-life problems demand statistical solutions, which in turn require new and profound mathematical methods. As such, the book is not only a collection of solved problems but also a source of new methods and their practical extensions. The authoritative contributions focus on analytical methods in statistics, asymptotics, estimation and Fisher information, robustness, stochastic models and inequalities, and other related fields; further, they address e.g. average autoregression quantiles, neural networks, weighted empirical minimum distance estimators, implied volatility surface estimation, the Grenander estimator, non-Gaussian component analysis, meta learning, and high-dimensional errors-in-variables models.
Editors and Affiliations
About the editors
Matúš Maciak is an Assistant Professor at the Department of Probability and Mathematical Statistics, Charles University, Prague, Czech Republic. His research interests include innovative statistical approaches concerning nonparametric and semiparametric regression models, sparse fitting via convex optimization (atomic pursuit / LASSO), estimation under various shape constraints, robustness and quantiles, and changepoint detection and estimation within various data structures. He also has practical experience in applied statistics, especially in empirical econometrics and finance, insurance, ecology, and the medical sciences.
Michal Pešta is an Associate Professor at the Department of Probability and Mathematical Statistics, Charles University, Prague, Czech Republic. His research interests include asymptotic methods for changepoint, weak dependence, copulae, resampling methods, panel data, nonparametric regression, and errors-in-variables modeling. He is also interested in developing complex statistical methodology frameworks for various real-life settings, including empirical econometrics, finance, and non-life insurance.
Martin Schindler is an Assistant Professor of Applied Mathematics at the Technical University of Liberec, Czech Republic. His research interests include robust and nonparametric statistics, statistical computing and simulations. He has also worked on various inference procedures based on regression rank scores used in both linear and nonlinear models. During his postdoctoral studies at the University of Tampere he worked on nonparametric procedures for microarray data.
Bibliographic Information
Book Title: Analytical Methods in Statistics
Book Subtitle: AMISTAT, Liberec, Czech Republic, September 2019
Editors: Matúš Maciak, Michal Pešta, Martin Schindler
Series Title: Springer Proceedings in Mathematics & Statistics
DOI: https://doi.org/10.1007/978-3-030-48814-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-48813-0Published: 20 July 2020
Softcover ISBN: 978-3-030-48816-1Published: 21 July 2021
eBook ISBN: 978-3-030-48814-7Published: 19 July 2020
Series ISSN: 2194-1009
Series E-ISSN: 2194-1017
Edition Number: 1
Number of Pages: X, 156
Number of Illustrations: 7 b/w illustrations, 8 illustrations in colour
Topics: Statistical Theory and Methods, Probability Theory and Stochastic Processes, Applications of Mathematics, Statistics and Computing/Statistics Programs, Applied Statistics