數理統計（第2版）（英文版） [Mathematical Statistics(Second Edition)] epub pdf mobi txt 電子書 下載 2024

數理統計（第2版）（英文版） [Mathematical Statistics(Second Edition)] epub pdf mobi txt 電子書 下載 2024

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數理統計（第2版）（英文版） [Mathematical Statistics(Second Edition)] epub pdf mobi txt 電子書 下載 2024

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內容簡介

　　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。

內頁插圖

目錄

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．

數理統計（第2版）（英文版） [Mathematical Statistics(Second Edition)] epub pdf mobi txt 電子書 下載 2024

數理統計（第2版）（英文版） [Mathematical Statistics(Second Edition)] 下載 epub mobi pdf txt 電子書

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薛老師在自序中寫道，“我是特意捕捉瞭清風、樂聲和野芳，錄在這裏，專門用於鼓勵自己，就算是不定期地給自己獻一小朵小花吧——真誠美麗的文字，正是心靈開齣的花朵。”又一次闡述瞭她的人生信念——-真誠。讀瞭全書，給我最深刻的感受也是她的真誠，我看到瞭一個真實的人，一個真誠的老師。當學生的時候，老師是權威，跟老師的交往總是處於嚮上看的狀態，學生是一定要小心翼翼的，多數情況還是聽老師說的多，自己發錶意見少。現在當瞭傢長，為瞭孩子跟老師也沒少打交道，但是一直覺得自己好多真實的想法不敢說，老師呢，說齣來的也有一些讓人覺得是官話套話。看瞭這本書，我想我們也許都錯瞭，老師跟學生、老師跟傢長，平等地真誠的交流其實並不難。工作是艱辛——往往也是孤獨的。可是，於飛塵的間隙也有清風，於喧嚷的中間也有樂聲，於荊棘的叢中也有野芳。我是特意捕捉瞭清風、樂聲和野芳，錄在這裏，專門用於鼓勵自己，就算是不定期地給自己獻一朵小花吧——真誠美麗的文字，正是心靈開齣的花朵。也有沉重和迷惘。但我的文字，往往略掉瞭疲憊、沮喪和睏苦——無涉乎誠實、全麵與否，這是我的選擇——有意的，我將目光投在瞭值得的地方，心得體會

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非常好的書 就是比較難

評分☆☆☆☆☆

好，速度很快，上午下單，下午收貨，

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很好的一本書，值得買，還是搞活動買的。 學習有幫助

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基於測度論的數理統計基於測度論的數理統計

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老師推薦。。。

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經典好書，值得收藏

类似图書 點擊查看全場最低價