数理统计(第2版)(英文版) [Mathematical Statistics(Second Edition)]

数理统计(第2版)(英文版) [Mathematical Statistics(Second Edition)] 下载 mobi epub pdf 电子书 2024


简体网页||繁体网页
[美] 邵 著



点击这里下载
    


想要找书就要到 图书大百科
立刻按 ctrl+D收藏本页
你会得到大惊喜!!

发表于2024-11-05

类似图书 点击查看全场最低价

图书介绍

出版社: 世界图书出版公司
ISBN:9787510005343
版次:1
商品编码:10104512
包装:平装
外文名称:Mathematical Statistics(Second Edition)
开本:24开
出版时间:2009-10-01
用纸:胶版纸
页数:591
正文语种:英语


相关图书





图书描述

内容简介

  Probability Theory、Probability Spaces and Random Elements、σ-fields and measures、Measurable functions and distributions、Integration and Differentiation、Integration、Radon.Nikodym derivative、Distributions and Their Characteristics、Distributions and probability densities、Moments and moment inequalities、Moment generating and characteristic functions、onditional Expectations、Conditional expectations、Independence、Conditional distributions、Markov chains and martingales、Asymptotic Theory、Convergence modes and stochastic orders等等。

内页插图

目录

Preface to the First Edition
Preface to the Second Edition
Chapter 1.Probability Theory
1.1 Probability Spaces and Random Elements
1.1.1σ-fields and measures
1.1.2 Measurable functions and distributions
1.2 Integration and Differentiation
1.2.1 Integration
1.2.2 Radon.Nikodym derivative
1.3 Distributions and Their Characteristics
1.3.1 Distributions and probability densities
1.3.2 Moments and moment inequalities
1.3.3 Moment generating and characteristic functions
1.4 Conditional Expectations
1.4.1 Conditional expectations
1.4.2 Independence
1.4.3 Conditional distributions
1.4.4 Markov chains and martingales
1.5 Asymptotic Theory
1.5.1 Convergence modes and stochastic orders
1.5.2 Weak convergence
1.5.3 Convergence of transformations
1.5.4 The law of large numbers
1.5.5 The central limit theorem
1.5.6 Edgeworth and Cornish-Fisher expansions
1.6 Exercises

Chapter 2. Fundamentals of Statistics
2.1 Populations,Samples,and Models
2.1.1 Populations and samples
2.1.2 Parametric and nonparametric models
2.1.3 Exponential and location.scale families
2.2 Statistics.Sufficiency,and Completeness
2.2.1 Statistics and their distributions
2.2.2 Sufficiency and minimal sufficiency
2.2.3 Complete statistics
2.3 Statistical Decision Theory
2.3.1 Decision rules,lOSS functions,and risks
2.3.2 Admissibility and optimality
2.4 Statistical Inference
2.4.1 P0il)t estimators
2.4.2 Hypothesis tests
2.4.3 Confidence sets
2.5 Asymptotic Criteria and Inference
2.5.1 Consistency
2.5.2 Asymptotic bias,variance,and mse
2.5.3 Asymptotic inference
2.6 Exercises

Chapter 3.Unbiased Estimation
3.1 The UMVUE
3.1.1 Sufficient and complete statistics
3.1.2 A necessary and.sufficient condition
3.1.3 Information inequality
3.1.4 Asymptotic properties of UMVUEs
3.2 U-Statistics
3.2.1 Some examples
3.2.2 Variances of U-statistics
3.2.3 The projection method
3.3 The LSE in Linear Models
3.3.1 The LSE and estimability
3.3.2 The UMVUE and BLUE
3.3.3 R0bustness of LSEs
3.3.4 Asymptotic properties of LSEs
3.4 Unbiased Estimators in Survey Problems
3.4.1 UMVUEs of population totals
3.4.2 Horvitz-Thompson estimators
3.5 Asymptotically Unbiased Estimators
3.5.1 Functions of unbiased estimators
3.5.2 The method ofmoments
3.5.3 V-statistics
3.5.4 The weighted LSE
3.6 Exercises

Chapter 4.Estimation in Parametric Models
4.1 Bayes Decisions and Estimators
4.1.1 Bayes actions
4.1.2 Empirical and hierarchical Bayes methods
4.1.3 Bayes rules and estimators
4.1.4 Markov chain Mollte Carlo
4.2 Invariance......
4.2.1 One-parameter location families
4.2.2 One-parameter seale families
4.2.3 General location-scale families
4.3 Minimaxity and Admissibility
4.3.1 Estimators with constant risks
4.3.2 Results in one-parameter exponential families
4.3.3 Simultaneous estimation and shrinkage estimators
4.4 The Method of Maximum Likelihood
4.4.1 The likelihood function and MLEs
4.4.2 MLEs in generalized linear models
4.4.3 Quasi-likelihoods and conditional likelihoods
4.5 Asymptotically Efficient Estimation
4.5.1 Asymptotic optimality
4.5.2 Asymptotic efficiency of MLEs and RLEs
4.5.3 Other asymptotically efficient estimators
4.6 Exercises

Chapter 5.Estimation in Nonparametric Models
5.1 Distribution Estimators
5.1.1 Empirical C.d.f.s in i.i.d.cases
5.1.2 Empirical likelihoods
5.1.3 Density estimation
5.1.4 Semi-parametric methods
5.2 Statistical Functionals
5.2.1 Differentiability and asymptotic normality
5.2.2 L-.M-.and R-estimators and rank statistics
5.3 Linear Functions of Order Statistics
5.3.1 Sample quantiles
5.3.2 R0bustness and efficiency
5.3.3 L-estimators in linear models
5.4 Generalized Estimating Equations
5.4.1 The GEE method and its relationship with others
5.4.2 Consistency of GEE estimators
5.4.3 Asymptotic normality of GEE estimators
5.5 Variance Estimation
5.5.1 The substitution.method
5.5.2 The jackknife
5.5.3 The bootstrap
5.6 Exercises

Chapter 6.Hypothesis Tests
6.1 UMP Tests
6.1.1 The Neyman-Pearson lemma
6.1.2 Monotone likelihood ratio
6.1.3 UMP tests for two-sided hypotheses
6.2 UMP Unbiased Tests
6.2.1 Unbiasedness,similarity,and Neyman structure
6.2.2 UMPU tests in exponential families
6.2.3 UMPU tests in normal families
……
Chapter 7 Confidence Sets
References
List of Notation
List of Abbreviations
Index of Definitions,Main Results,and Examples
Author Index
Subject Index

前言/序言

  This book is intended for a course entitled Mathematical Statistics offered at the Department of Statistics,University of Wisconsin.Madison.This course,taught in a mathematically rigorous fashion,covers essential materials in statistical theory that a first or second year graduate student typicallY needs to learn as preparation for work on a Ph.D.degree in statistics.The course is designed for two 15-week semesters.with three lecture hours and two discussion hours in each week. Students in this course are assumed to have a good knowledge of advanced calgulus.A course in real analy.sis or measure theory prior to this course is often recommended.Chapter 1 provides a quick overview of important concepts and results in measure-theoretic probability theory that are used as tools in mathematical statistics.Chapter 2 introduces some fundamental concepts in statistics,including statistical models.the principle of SUfIlciency in data reduction,and two statistical approaches adopted throughout the book: statistical decision theory and statistical inference.
  Each of Chapters 3 through 7 provides a detailed study of an important topic in statistical decision theory and inference:Chapter 3 introduces the theory of unbiased estimation;Chapter 4 studies theory and methods in point estimation ander parametric models;Chapter 5 covers point estimation in nonparametric settings;Chapter 6 focuses on hypothesis testing;and Chapter 7 discusses interval estimation and confidence sets.The classical frequentist approach is adopted in this book.although the Bayesian approach is also introduced (§2.3.2,§4.1,§6.4.4,and§7.1.3).Asymptotic(1arge sample)theory,a crucial part of statistical inference,is studied throughout the book,rather than in a separate chapter.
  About 85%of the book covers classical results in statistical theory that are typically found in textbooks of a similar level.These materials are in the Statistics Department’S Ph.D.qualifying examination syllabus.

数理统计(第2版)(英文版) [Mathematical Statistics(Second Edition)] 下载 mobi epub pdf txt 电子书 格式

数理统计(第2版)(英文版) [Mathematical Statistics(Second Edition)] mobi 下载 pdf 下载 pub 下载 txt 电子书 下载 2024

数理统计(第2版)(英文版) [Mathematical Statistics(Second Edition)] 下载 mobi pdf epub txt 电子书 格式 2024

数理统计(第2版)(英文版) [Mathematical Statistics(Second Edition)] 下载 mobi epub pdf 电子书
想要找书就要到 图书大百科
立刻按 ctrl+D收藏本页
你会得到大惊喜!!

用户评价

评分

这本书还没看啊,刚收到,只能说包装很好。别人推荐的。

评分

正版图书,质量很好,京东活动力度大,赞!

评分

商品效果不错,值得推荐。

评分

很不错,物流很快,是正版

评分

英文影印版,原汁原味,从某种程度上甚至比汉译版好懂。

评分

很好,重新学习一下。

评分

比较偏理论的原版书,只能慢慢啃了

评分

权威书籍,参考价值高!!!!!!印刷也不错!!!!!!!!!!!!

评分

书不错,是正版 很满意~~~

类似图书 点击查看全场最低价

数理统计(第2版)(英文版) [Mathematical Statistics(Second Edition)] mobi epub pdf txt 电子书 格式下载 2024


分享链接








相关图书


本站所有内容均为互联网搜索引擎提供的公开搜索信息,本站不存储任何数据与内容,任何内容与数据均与本站无关,如有需要请联系相关搜索引擎包括但不限于百度google,bing,sogou

友情链接

© 2024 book.teaonline.club All Rights Reserved. 图书大百科 版权所有