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自己购的并整理上传，故而现收取一定费用，一共三本著作：
@2001 Bickel, Mathematical_Statistics_Basic_Ideas_and_Selected_Topics_I 数理统计基本概念及专题 卷1
@2016 Bickel, Mathematical_Statistics_Basic_Ideas_and_Selected_Topics_II 数理统计基本概念及专题 卷2
@2013 J Fan, Selected works of_Peter J. Bickel, Jianqing Fan,Ya'acov_Ritov,Chien Fu JeffWu Springer
统计大师Bickel--文选(Jianqing Fan编),数理统计基本概念1 2卷（2001版和2016版）,一共三本.rar
(17.92 MB, 需要: 60 个论坛币)
本附件包括：- @2016Bickel, Mathematical_Statistics_Basic_Ideas_and_Selected_Topics_II.pdf
- @2001Bickel, Mathematical_Statistics_Basic_Ideas_and_Selected_Topics_I 数理统计基本概念及专题 卷1.pdf
- @2013J Fan, Selected works of_Peter J. Bickel, Jianqing_Fan,Ya'acov_Ritov,Chien Fu JeffWu Springer.pdf
作者简介：
Peter J. Bickel， is a professor emeritus in the Department of Statistics and a professor in the Graduate School at the University of California, Berkeley. Dr. Bickel is a member of the American Academy of Arts and Sciences and the National Academy of Sciences. He has been a Guggenheim Fellow and MacArthur Fellow, a recipient of the COPSS Presidents’ Award, and president of the Bernoulli Society and the Institute of Mathematical Statistics. He holds honorary doctorate degrees from the Hebrew University of Jerusalem and ETH Zurich.
Kjell A. Doksum， is a senior scientist in the Department of Statistics at the University of Wisconsin–Madison. His research encompasses the estimation of nonparametric regression and correlation curves, inference for global measures of association in semiparametric and nonparametric settings, the estimation of regression quantiles, statistical modeling and analysis of HIV data, the analysis of financial data, and Bayesian nonparametric inference.
Jianqing Fan, is a statistician, financial econometrician, and data scientist. He is Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and Professor of Operations Research and Financial Engineering at the Princeton University where he chaired the department from 2012 to 2015. He is the winner of The 2000 COPSS Presidents' Award, Morningside Gold Medal for Applied Mathematics (2007), Guggenheim Fellow (2009), Pao-Lu Hsu Prize (2013) and Guy Medal in Silver (2014). He got elected to Academician from Academia Sinica in 2012.
注：Bickel等人将该书定位为“基于线性代数、矩阵论及高等微积分（不要求测度论）的、侧重于解决实际统计学问题的当代数理统计学教科书”。为此，它提供更可能多的细节，包括证明、演算、统计量之大样本表现等。（1）@2001 Bickel, Mathematical_Statistics_Basic_Ideas_and_Selected_Topics_I 数理统计基本概念及专题 卷1全书共分6章：
第二版前言：卷Ⅰ
第一版前言
1 统计模型、目标与性能准则
1.1 数据、模型、参数与统计量
1.1.1 数据与模型
1.1.2 参数化与参数
1.1.3 统计量——样本空间的函数
1.1.4 例子、回归模型
1.2 贝叶斯模型
1.3 决策论框架
1.3.1 决策论框架的组成部分
1.3.2 各种决策过程的比较
1.3.3 贝叶斯准则与极小极大准则
1.4 预测
1.5 充分陛
1.6 指数族
1.6.1 单参数情况
1.6.2 多参数情况
1.6.3 构造指数族
1.6.4 指数族的性质
1.6.5 共轭先验分布族
1.7 问题与补充
1.8 注释
1.9 参考文献
2 关于估计的方法
2.1 关于估计的基本方法
2.1.1 最小对比估计；估计方程式
2.1.2 替代原则与扩展原则
2.2 最小对比估计与估计方程
2.2.1 最小二乘估计与加权最小二乘估计
2.2.2 极大似然估计
2.3 多参数指数族的极大似然估计
2.4 关于算法的问题
2.4.1 对分法
2.4.2 坐标上升
2.4.3 Newton-Raphson算法
2.4.4 EM（期望／最大化）算法
2.5 问题与补充
2.6 注释
2.7 参考文献
3 性能的度量
3.1 介绍
3.2 贝叶斯方法
3.3 极小极大方法
3.4 无偏估计与风险不等式
3.4.1 无偏估计、抽样调查
3.4.2 信息不等式
3.5 非决定论准则
3.5.1 计算
3.5.2 解释性
3.5.3 稳健性
3.6 问题与补充
3.7 注释
3.8 参考文献
4 检验与置信域
4.1 介绍
4.2 选择检验统计量：Neyman-Pearson引理
4.3 一致最优势检验与单调似然比模型
4.4 置信限、置信区间、置信域
4.5 置信域与检验之间的对偶性
4.6 一致最精确置信限
4.7 频率派与贝叶斯派的公式
4.8 预测区间
4.9 似然比方法
4.9.1 介绍
4.9.2 正态分布配对试验的均值检验
4.9.3 两个正态总体均值差的检验及其置信区间
4.9.4 方差不相等的二样本问题
4.9.5 二元正态分布的似然比方法
4.10 问题与补充
4.11 注释
4.12 参考文献
5 渐近近似
5.1 介绍：渐近的意义与使用
5.2 一致性
5.2.1 指数族模型的替代估计与极大似然估计
5.2.2 最小对比估计的一致性
5.3 一阶与高阶渐近：德尔塔方法及其应用
5.3.1 矩的德尔塔方法
5.3.2 依分布近似的德尔塔方法
5.3.3 指数族的极大似然估计的渐近正态性
5.4 一维的渐近理论
5.4.1 估计：多项分布情况
5.4.2 最小对比估计与M估计的渐近正态性
5.4.3 极大似然估计的渐近正态性与有效性
5.4.4 检验
5.4.5 置信界限
5.5 后验分布的渐近性质与最优性
5.6 问题与补充
5.7 注释
5.8 参考文献
6 多参数情况的推断
6.1 高斯线性模型的推断
6.1.1 经典的高斯线性模型
6.1.2 估计
6.1.3 检验与置信区间
6.2 p维空间中的渐近估计理论
6.2.1 估计方程
6.2.2 极大似然估计的渐近正态性与有效性
6.2.3 多参数情况的后验分布
6.3 大样本检验与置信域
6.3.1 似然比统计量的分布的渐近近似
6.3.2 Wald与Rao的大样本检验
6.4 离散数据的大样本检验
6.4.1 多项分布模型的拟合优度。Pearson's X2检验
6.4.2 复合多项分布模型的拟合优度。列联表
6.4.3 二元响应的Logistic回归
6.5 广义线性模型
6.6 稳健性性质与半参数模型
6.7 问题与补充
6.8 注释
6.9 参考文献
A 基础概率论的回顾
B 概率与分析的其他主题
可以说，对任何有志于当代统计学研究的读者而言，这本书都能提供多方面有益的指导。
（2）@2016 Bickel, Mathematical_Statistics_Basic_Ideas_and_Selected_Topics_II 数理统计基本概念及专题 卷2
（这本书把大规模数据、高维统计融入高等统计教材的教材。还包括了当下统计一些热门的话题和方法。内容包括：经验过程，不变估计，半参数，蒙特卡洛，非参数，机器学习，变量选择等，有丰富的习题和补充阅读材料。）
The book presents important statistical concepts, methods, and tools not covered in the authors’ previous volume. This second volume focuses on inference in non- and semiparametric models. It not only reexamines the procedures introduced in the first 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.
The book 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 develops the theory of semiparametric maximum likelihood estimation with applications to areas such as survival analysis. It also discusses methods of inference based on sieve models and asymptotic testing theory. The remainder of the book is devoted to model and variable selection, Monte Carlo methods, nonparametric curve estimation, 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, 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 I, measure theory is not required for understanding.
Check out Volume I for fundamental, classical statistical concepts leading to the material in this volume.
(3 )@2013 J Fan, Selected works of_Peter J. Bickel, Jianqing Fan,Ya'acov_Ritov,Chien Fu JeffWu Springer
This volume presents selections of Peter J. Bickel’s major papers, along with comments on their novelty and impact on the subsequent development of statistics as a discipline. Each of the eight parts concerns a particular area of research and provides new commentary by experts in the area. The parts range from Rank-Based Nonparametrics to Function Estimation and Bootstrap Resampling. Peter’s amazing career encompasses the majority of statistical developments in the last half-century or about about half of the entire history of the systematic development of statistics. This volume shares insights on these exciting statistical developments with future generations of statisticians. The compilation of supporting material about Peter’s life and work help readers understand the environment under which his research was conducted. The material will also inspire readers in their own research-based pursuits. This volume includes new photos of Peter Bickel, his biography, publication list, and a list of his students. These give the reader a more complete picture of Peter Bickel as a teacher, a friend, a colleague, and a family man.
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