Analytical Methods in Statistics: Amistat, Liberec, Czech Republic, September 2019 - Couverture rigide

Couverture rigide

ISBN 10 : 3030488136 ISBN 13 : 9783030488130

Editeur : Springer Nature Switzerland AG, 2020

Synopsis

Preface.- Y. Güney, J. Jurečková and O. Arslan, Averaged Autoregression Quantiles in Autoregressive Model.- J. Kalina and P. Vidnerová, Regression Neural Networks with a Highly Robust Loss Function.- H. L. Koul and P. Geng, Weighted Empirical Minimum Distance Estimators in Berkson Measurement Error Regression Models.- M. Maciak, M. Pesta and S. Vitali, Implied Volatility Surface Estimation via Quantile Regularization.- I. Mizera, A remark on the Grenander estimator.- U. Radojičic and K. Nordhausen, Non-Gaussian Component Analysis: Testing the Dimension of the Signal Subspace.- P. Vidnerová, J. Kalina and Y. Güney, A Comparison of Robust Model Choice Criteria within a Metalearning Study.- S. Zwanzig and R. Ahmad, On Parameter Estimation for High Dimensional Errors-in-Variables Models.

A propos du livre

À propos de l?auteur

Matús 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 Pesta 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.

Détails de l'édition

• Éditeur: Springer Nature Switzerland AG
• Date d'édition: 2020
• Langue: anglais
• ISBN 10: 3030488136
• ISBN 13: 9783030488130
• Reliure: Relié
• Nombre de pages: 166
• Éditeur: Maciak Matúš, Pešta Michal, Schindler Martin
• Coordonnées du fabricant: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Allemagne