04508nam 22006615 450 991048448520332120200719174325.03-030-48814-410.1007/978-3-030-48814-7(CKB)4100000011354661(DE-He213)978-3-030-48814-7(MiAaPQ)EBC6273170(PPN)269148779(EXLCZ)99410000001135466120200719d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierAnalytical Methods in Statistics AMISTAT, Liberec, Czech Republic, September 2019 /edited by Matúš Maciak, Michal Pešta, Martin Schindler1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (X, 156 p. 15 illus., 8 illus. in color.) Springer Proceedings in Mathematics & Statistics,2194-1009 ;3293-030-48813-6 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. Pešta and S. Vitali, Implied Volatility Surface Estimation via Quantile Regularization -- I. Mizera, A remark on the Grenander estimator -- U. Radojičić 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.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.Springer Proceedings in Mathematics & Statistics,2194-1009 ;329Statistics ProbabilitiesApplied mathematicsEngineering mathematicsStatistical Theory and Methodshttps://scigraph.springernature.com/ontologies/product-market-codes/S11001Probability Theory and Stochastic Processeshttps://scigraph.springernature.com/ontologies/product-market-codes/M27004Applications of Mathematicshttps://scigraph.springernature.com/ontologies/product-market-codes/M13003Statistics and Computing/Statistics Programshttps://scigraph.springernature.com/ontologies/product-market-codes/S12008Applied Statisticshttps://scigraph.springernature.com/ontologies/product-market-codes/S17000Statistics .Probabilities.Applied mathematics.Engineering mathematics.Statistical Theory and Methods.Probability Theory and Stochastic Processes.Applications of Mathematics.Statistics and Computing/Statistics Programs.Applied Statistics.519.5Maciak Matúšedthttp://id.loc.gov/vocabulary/relators/edtPešta Michaledthttp://id.loc.gov/vocabulary/relators/edtSchindler Martinedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910484485203321Analytical Methods in Statistics1562363UNINA