LEADER 04508nam 22006615 450 001 9910484485203321 005 20200719174325.0 010 $a3-030-48814-4 024 7 $a10.1007/978-3-030-48814-7 035 $a(CKB)4100000011354661 035 $a(DE-He213)978-3-030-48814-7 035 $a(MiAaPQ)EBC6273170 035 $a(PPN)269148779 035 $a(EXLCZ)994100000011354661 100 $a20200719d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalytical Methods in Statistics $eAMISTAT, Liberec, Czech Republic, September 2019 /$fedited by Matú? Maciak, Michal Pe?ta, Martin Schindler 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (X, 156 p. 15 illus., 8 illus. in color.) 225 1 $aSpringer Proceedings in Mathematics & Statistics,$x2194-1009 ;$v329 311 $a3-030-48813-6 327 $aPreface -- Y. Güney, J. Jure?ková 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. Pe?ta and S. Vitali, Implied Volatility Surface Estimation via Quantile Regularization -- I. Mizera, A remark on the Grenander estimator -- U. Radoji?i? 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. 330 $aThis book collects peer-reviewed contributions on modern statistical methods and topics, stemming from the third workshop on Analytical Methods in Statistics, AMISTAT 2019, held in Liberec, Czech Republic, on September 16-19, 2019. Real-life problems demand statistical solutions, which in turn require new and profound mathematical methods. As such, the book is not only a collection of solved problems but also a source of new methods and their practical extensions. The authoritative contributions focus on analytical methods in statistics, asymptotics, estimation and Fisher information, robustness, stochastic models and inequalities, and other related fields; further, they address e.g. average autoregression quantiles, neural networks, weighted empirical minimum distance estimators, implied volatility surface estimation, the Grenander estimator, non-Gaussian component analysis, meta learning, and high-dimensional errors-in-variables models. 410 0$aSpringer Proceedings in Mathematics & Statistics,$x2194-1009 ;$v329 606 $aStatistics  606 $aProbabilities 606 $aApplied mathematics 606 $aEngineering mathematics 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 606 $aApplications of Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M13003 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aApplied Statistics$3https://scigraph.springernature.com/ontologies/product-market-codes/S17000 615 0$aStatistics . 615 0$aProbabilities. 615 0$aApplied mathematics. 615 0$aEngineering mathematics. 615 14$aStatistical Theory and Methods. 615 24$aProbability Theory and Stochastic Processes. 615 24$aApplications of Mathematics. 615 24$aStatistics and Computing/Statistics Programs. 615 24$aApplied Statistics. 676 $a519.5 702 $aMaciak$b Matú?$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPe?ta$b Michal$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSchindler$b Martin$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484485203321 996 $aAnalytical Methods in Statistics$91562363 997 $aUNINA