发帖
雷达卡
by Rabi Bhattacharya (Author), Lizhen Lin (Author), Victor Patrangenaru (Author)
This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics.
Part I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics - parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods.
Table of contents (15 chapters)
Introduction
Decision Theory
Introduction to General Methods of Estimation
Sufficient Statistics, Exponential Families, and Estimation
Testing Hypotheses
Consistency and Asymptotic Distributions of Statistics
Large Sample Theory of Estimation in Parametric Models
Tests in Parametric and Nonparametric Models
The Nonparametric Bootstrap
Nonparametric Curve Estimation
Edgeworth Expansions and the Bootstrap
Fréchet Means and Nonparametric Inference on Non-Euclidean Geometric Spaces
Multiple Testing and the False Discovery Rate
Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory
Miscellaneous Topics
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