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书  名:概率统计高级教程 II 统计学基础
  • 作  者:
  • 出版时间: 2009-04-01
  • 出 版 社: 清华大学出版社
  • 字  数: 603 千字
  • 印  次: 1-1
  • 印  张: 26
  • 开  本: 16开
  • ISBN: 9787302195016
  • 装  帧: 平装
  • 定  价:¥49.00
电子书价:¥34.30 折扣:70折 节省:¥14.70 vip价:¥34.30 电子书大小:2.05M
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内容简介
  · This is an update Text book for beginning graduate students in Mathematics, Probability and Statistics, Engineering, Computer Sciences, Mathematical Economics
  · It distinguishes from all existing texts on the subject from its pedagogical spirit, namely, motivations before mathematics; mathematics tools are only introduced when needed and motivated
  · All theoretical results are proved in a friendly fashion
  · Teaching the students, not only the concepts and possible applications, but also guiding the students with proof techniques
  · This series will help students to learn with full understanding and appreciation of the subject
  · It will provide interested students with solid background for research
前言
  
This Volume II is the second half of a text for a course in
statistics at the beginning graduate level. Statistics is a man-made
science aiming at assisting humans in making decisions in the face
of uncertainty. This science is built upon the rigorous theory of
probability as described in Volume I. Thus, in studying this text,
students should consult Volume I whenever needed.

As stated in the preface of Volume I, there are various reasons to write
another text in statistics at the introductory level. An obvious reason is
to make the topic of statistics pleasant for students!

In an introductory course in statistics such as this one, one can
only include basic ideas, concepts, procedures and applications at a
very standard level. By this we mean that only the topics of
estimation, hypothesis testing and prediction are included. Also,
all inference procedures are developed for the standard type of
data, namely precise observations which are numerical or
vector-valued. The students should easily recognize that it is the
data which dictate the developed statistical procedures in this
text. Thus, other types of data, such as censored data in survival
analysis, missing data in questionnaires, coarse data in
biostatistics, imprecise data (or partially observed data, such as
those occurring in the problem of identification of DNA sequences in
bioinformatics, using hidden Markov models), and perception-based
data (which are expressed linguistically) will not be discussed.
However, the methodology for precise data clearly indicates the
general framework for analyzing other types of data. After all,
statistics is a science of data analysis.

With the rapid advances of technology, the use of statistics has
been extended to many new emerging applications, both in physical
and social sciences. The text does not cover these new statistical
techniques. The text is written as a pedagogical source for
instruction at universities. A solid understanding of statistics, at
the simplest level, will open the door for embarking on any new
problems which call for statistical assistance.
目录

鐩綍
Preface
1 An Invitation to Statistics
銆1.1 A Motivating Example
銆1.2 Generalities on Survey Sampling
銆1.3 Statistical Data
銆1.4 Statistical Models
銆1.5 Some Computational Statistics
銆1.6 Exercises
2銆Sampling Distributions
銆2.1 Sampling from a Bernoulli Population
銆2.2 Sampling from a Normal Population
銆2.3 Sampling from an Exponential Population
銆2.4 Order StatisticS
銆2.5 Distributions of Quadratic Forms
銆2.6 Exercises
3 Data Reduction
銆3.1 Sufficient Statistics
銆3.2 Complete Statistics
銆3.3 Exponential and Location-scale Families
銆3.4 Exercises
4 Estimation
銆4.1 Point Estimation
銆4.2 The Best Unbiased Estimation
銆4.3 Fisher Information and Efficiency
銆4.4 Two Methods of Finding Estimators
銆4.5 Confidence Sets
銆4.6 Bayes Estimation
銆4.7 Exercises
5 Large Sample Estimation
銆5.1 Consistency
銆5.2 Asymptotic Normality
銆5.3 Asymptotic Normality of Maximum Likelihood Estimators
銆5.4 Asymptotic Efficiency
銆5.5 Large Sample Interval Estimation
銆5.6 Robust Estimation
銆5.7 Exercises
6 Tests of Statistical Hypotheses
7 Nonparametric Statistical Inference
A Common Distributions
B Some Common Statistical Tables
Bibliography
Index
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