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Analytical methods in statisticsAMIS...

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Analytical methods in statisticsAMISTAT, Liberec, Czech Republic, September 2019 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analytical methods in statisticsedited by Matus Maciak, Michal Pesta, Martin Schindler.
Reminder of title:	AMISTAT, Liberec, Czech Republic, September 2019 /
other author:	Maciak, Matus.
corporate name:
Published:	Cham :Springer International Publishing :2020.
Description:	x, 156 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Mathematical statistics
Online resource:	https://doi.org/10.1007/978-3-030-48814-7
ISBN:	9783030488147$q(electronic bk.)

Analytical methods in statisticsAMISTAT, Liberec, Czech Republic, September 2019 /
Analytical methods in statisticsAMISTAT, Liberec, Czech Republic, September 2019 /[electronic resource] :edited by Matus Maciak, Michal Pesta, Martin Schindler. - Cham :Springer International Publishing :2020. - x, 156 p. :ill., digital ;24 cm. - Springer proceedings in mathematics & statistics,v.3292194-1009 ;. - Springer proceedings in mathematics & statistics ;v.19..

Preface -- Y. Guney, J. Jureckova and O. Arslan, Averaged Autoregression Quantiles in Autoregressive Model -- J. Kalina and P. Vidnerova, 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. Radojicic and K. Nordhausen, Non-Gaussian Component Analysis: Testing the Dimension of the Signal Subspace -- P. Vidnerova, J. Kalina and Y. Guney, 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.

ISBN: 9783030488147$q(electronic bk.)

Standard No.: 10.1007/978-3-030-48814-7doiSubjects--Topical Terms:

182294
Mathematical statistics

LC Class. No.: QA276.A1 / A535 2019

Dewey Class. No.: 519.5

Analytical methods in statisticsAMISTAT, Liberec, Czech Republic, September 2019 /
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505 0 $a Preface -- Y. Guney, J. Jureckova and O. Arslan, Averaged Autoregression Quantiles in Autoregressive Model -- J. Kalina and P. Vidnerova, 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. Radojicic and K. Nordhausen, Non-Gaussian Component Analysis: Testing the Dimension of the Signal Subspace -- P. Vidnerova, J. Kalina and Y. Guney, 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 $a 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.
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