* Mathematical Statistics: Basic Ideas and Selected Topics, Volume 2 (PDF)* presents important statistical methods, concepts, and tools not covered in the authors’ previous volume. This 2nd volume focuses on inference in semi- and non-parametric models. It not only reexamines the procedures introduced in the 1st volume from a more sophisticated point of view but also addresses new problems originating from the analysis of estimation of functions and other complex decision procedures and large-scale data analysis.

It develops the theory of semiparametric maximum likelihood estimation with applications to areas such as survival analysis. The ebook *Mathematical Statistics: Basic Ideas and Selected Topics Vol. 2* also covers asymptotic efficiency in semiparametric models from the Le Cam and Fisherian points of view as well as some finite sample size optimality criteria based on Lehmann–Scheffé theory. It also discusses methods of inference based on sieve models and asymptotic testing theory. The remainder of the ebook is devoted to variable and model selection, nonparametric curve estimation, Monte Carlo methods, and prediction, classification, and machine learning topics. The necessary background material is included in an appendix.

Using the tools and methods developed in this textbook, college students will be ready for advanced research in modern statistics. Numerous examples illustrate statistical modeling and inference concepts while end-of-chapter problems reinforce elementary concepts and introduce important new topics. As in Volume 1, measure theory is not required for understanding.

*The solutions to exercises for Volume 2 are included in the back of the book.*

*Check out Volume I for classical, fundamental statistical concepts leading to the material in this volume.*

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**NOTE: This purchase only includes the PDF of Mathematical Statistics: Basic Ideas and Selected Topics Volume II.**

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