| 020 | $a9783030488147 |
| 082 | $a519.5 |
| 099 | $aOnline resource: Springer |
| 245 | $aAnalytical Methods in Statistics$h[EBook]$bAMISTAT, Liberec, Czech Republic, September 2019$cedited by Matúš Maciak, Michal Pešta, Martin Schindler. |
| 250 | $a1st ed. 2020. |
| 260 | $aCham$bSpringer International Publishing$c2020. |
| 300 | $aX, 156 p. 15 illus., 8 illus. in color.$bonline resource. |
| 336 | $atext |
| 338 | $aonline resource |
| 440 | $aSpringer Proceedings in Mathematics & Statistics,$x2194-1009 ;$v329 |
| 505 | $aPreface -- 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. |
| 520 | $aThis 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. |
| 533 | $aDigital book. Cham Springer Nature 2020. - Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format |
| 538 | $aOnline access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| 700 | $aMaciak, Matúš.$eeditor. |
| 700 | $aPešta, Michal.$eeditor. |
| 700 | $aSchindler, Martin.$eeditor. |
| 710 | $aSpringerLink (Online service) |
| 856 | $uhttps://doi.org/10.1007/978-3-030-48814-7 |
