Wolfgang Karl Härdle is Professor of Statistics at the Humboldt-Universität zu Berlin and the Director of CASE – the Centre for Applied Statistics and Economics. He teaches quantitative finance and semi-parametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI and an advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan. Vladimir Panov is a postdoctoral researcher at the University of Duisburg-Essen. His research interests include statistical inference on stochastic processes, especially on models based on Levy processes. Over the last several years he has worked as a research assistant at the Weierstrass Institute for Applied Analysis and Stochastics (Berlin), where he has focused on multidimensional statistical models.Vladimir Spokoiny is a Professor at the Humboldt University of Berlin and focuses on applicable mathematical statistics. Weining Wang is a postdoctoral researcher at CASE – the Centre for Applied Statistics and Economics, where she teaches quantitative finance and semi-parametric statistical methods. Her research focuses on quantile regression and high-dimensional nonparametric models.Weining Wang is a postdoctoral researcher at CASE – the Centre for Applied Statistics and Economics, where she teaches quantitative finance and semi-parametric statistical methods. Her research focuses on quantile regression and high-dimensional nonparametric models.

• ​Basics.- Parameter Estimation for an i.i.d. Model.- Parameter Estimation for a Regression Model.- Estimation in Linear Models.- Bayes Estimation.- Testing a Statistical Hypothesis.- Testing in Linear Models.- Some Other Testing Methods.

From the reviews: "The book 'Basics of model mathematical statistics' is built as a series of focused exercises revolving around parameter estimation, linear models, Bayesian estimation and statistical hypothesis testing. ... This book is a valuable resource for undergraduates and post-graduates alike. The detailed proofs and the R code and output make it a must have for the understanding of modern mathematical statistics." (Irina Ioana Mohorianu, zbMATH, Vol. 1286 (1), 2014)

Wolfgang Karl Härdle is Professor of Statistics at the Humboldt-Universität zu Berlin and the Director of CASE – the Centre for Applied Statistics and Economics. He teaches quantitative finance and semi-parametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI and an advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan. Vladimir Panov is a postdoctoral researcher at the University of Duisburg-Essen. His research interests include statistical inference on stochastic processes, especially on models based on Levy processes. Over the last several years he has worked as a research assistant at the Weierstrass Institute for Applied Analysis and Stochastics (Berlin), where he has focused on multidimensional statistical models.Vladimir Spokoiny is a Professor at the Humboldt University of Berlin and focuses on applicable mathematical statistics. Weining Wang is a postdoctoral researcher at CASE – the Centre for Applied Statistics and Economics, where she teaches quantitative finance and semi-parametric statistical methods. Her research focuses on quantile regression and high-dimensional nonparametric models.Weining Wang is a postdoctoral researcher at CASE – the Centre for Applied Statistics and Economics, where she teaches quantitative finance and semi-parametric statistical methods. Her research focuses on quantile regression and high-dimensional nonparametric models.

• ​Basics.- Parameter Estimation for an i.i.d. Model.- Parameter Estimation for a Regression Model.- Estimation in Linear Models.- Bayes Estimation.- Testing a Statistical Hypothesis.- Testing in Linear Models.- Some Other Testing Methods.

From the reviews: "The book 'Basics of model mathematical statistics' is built as a series of focused exercises revolving around parameter estimation, linear models, Bayesian estimation and statistical hypothesis testing. ... This book is a valuable resource for undergraduates and post-graduates alike. The detailed proofs and the R code and output make it a must have for the understanding of modern mathematical statistics." (Irina Ioana Mohorianu, zbMATH, Vol. 1286 (1), 2014)

Wolfgang Karl Härdle is Professor of Statistics at the Humboldt-Universität zu Berlin and the Director of CASE – the Centre for Applied Statistics and Economics. He teaches quantitative finance and semi-parametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI and an advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan. Vladimir Panov is a postdoctoral researcher at the University of Duisburg-Essen. His research interests include statistical inference on stochastic processes, especially on models based on Levy processes. Over the last several years he has worked as a research assistant at the Weierstrass Institute for Applied Analysis and Stochastics (Berlin), where he has focused on multidimensional statistical models.Vladimir Spokoiny is a Professor at the Humboldt University of Berlin and focuses on applicable mathematical statistics. Weining Wang is a postdoctoral researcher at CASE – the Centre for Applied Statistics and Economics, where she teaches quantitative finance and semi-parametric statistical methods. Her research focuses on quantile regression and high-dimensional nonparametric models.Weining Wang is a postdoctoral researcher at CASE – the Centre for Applied Statistics and Economics, where she teaches quantitative finance and semi-parametric statistical methods. Her research focuses on quantile regression and high-dimensional nonparametric models.

• ​Basics.- Parameter Estimation for an i.i.d. Model.- Parameter Estimation for a Regression Model.- Estimation in Linear Models.- Bayes Estimation.- Testing a Statistical Hypothesis.- Testing in Linear Models.- Some Other Testing Methods.

From the reviews: "The book 'Basics of model mathematical statistics' is built as a series of focused exercises revolving around parameter estimation, linear models, Bayesian estimation and statistical hypothesis testing. ... This book is a valuable resource for undergraduates and post-graduates alike. The detailed proofs and the R code and output make it a must have for the understanding of modern mathematical statistics." (Irina Ioana Mohorianu, zbMATH, Vol. 1286 (1), 2014)