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＜電子ブック＞
Analytical Methods in Statistics : AMISTAT, Liberec, Czech Republic, September 2019 / edited by Matúš Maciak, Michal Pešta, Martin Schindler
(Springer Proceedings in Mathematics & Statistics. ISSN:21941017 ; 329)

版	1st ed. 2020.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2020
大きさ	X, 156 p. 15 illus., 8 illus. in color : online resource
著者標目	Maciak, Matúš editor Pešta, Michal editor Schindler, Martin editor SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Probabilities LCSH:Mathematics LCSH:Mathematical statistics—Data processing FREE:Statistical Theory and Methods FREE:Probability Theory FREE:Applications of Mathematics FREE:Statistics and Computing FREE:Applied Statistics
一般注記	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 HTTP:URL=https://doi.org/10.1007/978-3-030-48814-7

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