普通高等教育"十二五"规划教材·浙江财经大学省级重点学科重点专业统计学系列教材:应用多元统计分析与R软件(英文版) 吴浪, 邱瑾 9787030412430,7030412435
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《普通高等教育"十二五"规划教材·浙江财经大学省级重点学科重点专业统计学系列教材:应用多元统计分析与R软件(英文版)》可作为统计学专业本科和研究生双语教材。
目录
Preface
Chapter 1 Introduction
1.1 Goal of Statistics
1.2 Univariate Analysis
1.3 Multivariate Analysis
1.4 Multivariate Normal Distribution
1.5 Unsupervised Learning and Supervised Learning
1.6 Data Analysis Strategies and Statistical Thinking
1.7 Outline
Exercises 1
Chapter 2 Principal Components Analysis
2.1 The Basic Idea
2.2 The Principal Components
2.3 Choose Number of Principal Components
2.4 Considerations in Data Analysis
2.5 Examples in R
Exercises 2
Chapter 3 Factor Analysis
3.1 The Basic Idea
3.2 The Factor Analysis Model
3.3 Methods for Estimation
3.4 Examples in R
Exercises 3
Chapter 4 Discriminant Analysis and Cluster Analysis
4.1 Introduction
4.2 Discriminant Analysis
4.3 Cluster Analysis
4.4 Examples in R
Exercises 4
Chapter 5 Inference for a Multivariate Normal Population
5.1 Introduction
5.2 Inference for Multivariate Means
5.3 Inference for Covariance Matrices
5.4 Large Sample Inferences about a Population Mean Vector
5.5 Examples in R
Exercises 5
Chapter 6 Discrete or Categorical Multivariate Data
6.1 Discrete or Categorical Data
6.2 The Multinomial Distribution
6.3 Contingency Tables
6.4 Associations Between Discrete or Categorical Variables
6.5 Logit Models for Multinomial Variables
6.6 Loglinear Models for Contingency Tables
6.7 Example in R
Exercises 6
Chapter 7 Copula Models
7.1 Introduction
7.2 Copula Models
7.3 Measures of Dependence
7.4 Applications in Actuary and Finance
7.5 Applications in Longitudinal and Survival Data*
7.6 Example in R
Exercises 7
Chapter 8 Linear and Nonlinear Regression Models
8.1 Introduction
8.2 Linear Regression Models
8.3 Model Selection
8.4 Model Diagnostics
8.5 Data Analysis Examples with R
8.6 Nonlinear Regression Models
8.7 More on Model Selection
Exercises 8
Chapter 9 Generalized Linear Models
9.1 Introduction
9.2 The Exponential Family
9.3 The General Form of a GLM
9.4 Inference for GLM
9.5 Model Selection and Model Diagnostics
9.6 Logistic Regression Models
9.7 Poisson Regression Models
Exercises 9
Chapter 10 Multivariate Regression and MANOVA Models
10.1 Introduction
10.2 Multivariate Regression Models
10.3 MANOVA Models
10.4 Examples in R
Exercises 10
Chapter 11 Longitudinal Data， Panel Data， and Repeated Measurements
11.1 Introduction
11.2 Methods for Longitudinal Data Analysis
11.3 Linear Mixed Effects Models
11.4 GEE Models
Exercises 11
Chapter 12 Methods for Missing Data
12.1 Missing Data Mechanisms
12.2 Methods for Missing Data
12.3 Multiple Imputation Methods
12.4 Multiple Imputation by Chained Equations
12.5 The EM Algorithm
12.6 Example in R
Exercises 12
Chapter 13 Robust Multivariate Analysis
13.1 The Need for Robust Methods
13.2 General Robust Methods
13.3 Robust Estimates of the Mean and Standard Deviation
13.4 Robust Estimates of the Covariance Matrix
13.5 Robust PCA and Regressions
13.6 Examples in R
Exercises 13
Chapter 14 Selected Topics
14.1 Likelihood Methods
14.2 Bootstrap Methods
14.3 MCMC Methods and the Gibbs Sampler
14.4 Survival Analysis
14.5 Data Science， Big Data， and Data Mining
References
Index
文摘
Chapter 1
Introduction
1.1 Goal of Statistics
A main goal of statistics is to analyze data in order to obtain useful information and make important decisions. In other words， statisticians analyze data to extract useful information from the data in a sample and draw important conclusions about the population. In modern world， many important decisions are based on information from data. With the developments of modern computers and internet， massive data are available and can be easily obtained， but important information in the data may not be easily obtained without using modern statistical methods. Therefore， statistics is becoming one of the most important subjects in the 21 century， and statistical methods are among the most widely used tools in almost every area， including banks，insurance industries， economics， finance， medicine， and engineering. As a New York Times article says (August 5， 2009， For Today's Graduate， Just One Word:Statistics): "For many different jobs in today's world， mostly what you do is data analysis (statistics)， even for jobs which seem unrelated to statistics .Many today's decisions in industry and government are based on data analysis results. Statisticians are thus in high demand ”.
Data can be collected in many ways， such as survey， internet， company records and designed experiments. Our goalis to analyze the data to extract as much information as possible and then draw some conclusions about the whole population. For example，if a new drug is found to be effective on 20 randomly selected patients (sample) based on statistical analysis， will this drug also be effective for all patients (population)?If exam scores are found to be related to students' IQ scores as well as students' attitude on 50 randomly selected students (sample)， is this also true for all students (population)? Such a generalization from sample to population is called statistical i'n，fere'n，ce. Sometimes， however， we may just wish to obtain useful information from the sample， without necessarily making inference about the population， especially if the sample is not a random and representative sample.In practice， data analysis often consists of two stages:
* exploratory data analysis.
* formal (or confirmatory) data analysis.
In exploratory data analyses， data are simply summarized using common statistics (e.g.， means， standard deviations， correlations) and are displayed using common graphical tools (e.g.， histograms， boxplots， scatterplots). In this stage， we simply present and summarize the data， without trying to generalize the conclusions ob- tained from summary statistics and graphs to the whole population. In this stage，we do not need to make any distributional assumptions for the data， i.e.， we do not need to assume that data follow certain distributions such as the normal distributions.Thus， the conclusions obtained from exploratory analysis do not depend on the va-lidity of any assumptions. Exploratory analysis can reveal important features of the data， which may lead to preliminary conclusions. Exploratory data analysis is an important step in any data analysis and should not be skipped. However，exploratory data analysis is usually followed by a formal or confirmatory analysis，which is used to confirm the preliminary conclusions from the exploratory analysis.In formal (or confirmatory) data analysis， we assume models or distributions for the data or population， estimate unknown parameters in the models or distributions，and attempt to make statisticalinference so that we may generalize the results based on the sample to the whole population. For example， we may assume that the popu-lation follows a normal distribution， and then we use data to estimate the parameters in the normal distribution (mean and variance). Note that the models or distributions are only assumptions， so they may not be true. In other words， the assumed mod-els and distributions should be checked for their validity based on the data. Since the assumed models or distributions rarely hold exactly， formal analysis results should only be viewed as approximate， and it is desirable to use different statistical models or methods to further validate the results.
In practice， data are often collected on many variables， such as age， income， and education. When we analyze data on each variable separately， the data on each vari-able are called ufn，ivariate data， and an analysis of univariate data is called un，ivariate a'n.alysis. In univariate analysis， the association or correlation between dif-ferent variables are ignored. In other words， when we analyze data on one variable in univariate analysis， we cannot borrow information from data of other variables. For example， suppose that we have data on income from a sample survey. In univariate analysis， we can compute the average income and the standard deviations. But if an income is missing or not reported， we cannot estimate it without information from other variables such as age an
《普通高等教育"十二五"规划教材·浙江财经大学省级重点学科重点专业统计学系列教材:应用多元统计分析与R软件(英文版)》可作为统计学专业本科和研究生双语教材。
目录
Preface
Chapter 1 Introduction
1.1 Goal of Statistics
1.2 Univariate Analysis
1.3 Multivariate Analysis
1.4 Multivariate Normal Distribution
1.5 Unsupervised Learning and Supervised Learning
1.6 Data Analysis Strategies and Statistical Thinking
1.7 Outline
Exercises 1
Chapter 2 Principal Components Analysis
2.1 The Basic Idea
2.2 The Principal Components
2.3 Choose Number of Principal Components
2.4 Considerations in Data Analysis
2.5 Examples in R
Exercises 2
Chapter 3 Factor Analysis
3.1 The Basic Idea
3.2 The Factor Analysis Model
3.3 Methods for Estimation
3.4 Examples in R
Exercises 3
Chapter 4 Discriminant Analysis and Cluster Analysis
4.1 Introduction
4.2 Discriminant Analysis
4.3 Cluster Analysis
4.4 Examples in R
Exercises 4
Chapter 5 Inference for a Multivariate Normal Population
5.1 Introduction
5.2 Inference for Multivariate Means
5.3 Inference for Covariance Matrices
5.4 Large Sample Inferences about a Population Mean Vector
5.5 Examples in R
Exercises 5
Chapter 6 Discrete or Categorical Multivariate Data
6.1 Discrete or Categorical Data
6.2 The Multinomial Distribution
6.3 Contingency Tables
6.4 Associations Between Discrete or Categorical Variables
6.5 Logit Models for Multinomial Variables
6.6 Loglinear Models for Contingency Tables
6.7 Example in R
Exercises 6
Chapter 7 Copula Models
7.1 Introduction
7.2 Copula Models
7.3 Measures of Dependence
7.4 Applications in Actuary and Finance
7.5 Applications in Longitudinal and Survival Data*
7.6 Example in R
Exercises 7
Chapter 8 Linear and Nonlinear Regression Models
8.1 Introduction
8.2 Linear Regression Models
8.3 Model Selection
8.4 Model Diagnostics
8.5 Data Analysis Examples with R
8.6 Nonlinear Regression Models
8.7 More on Model Selection
Exercises 8
Chapter 9 Generalized Linear Models
9.1 Introduction
9.2 The Exponential Family
9.3 The General Form of a GLM
9.4 Inference for GLM
9.5 Model Selection and Model Diagnostics
9.6 Logistic Regression Models
9.7 Poisson Regression Models
Exercises 9
Chapter 10 Multivariate Regression and MANOVA Models
10.1 Introduction
10.2 Multivariate Regression Models
10.3 MANOVA Models
10.4 Examples in R
Exercises 10
Chapter 11 Longitudinal Data， Panel Data， and Repeated Measurements
11.1 Introduction
11.2 Methods for Longitudinal Data Analysis
11.3 Linear Mixed Effects Models
11.4 GEE Models
Exercises 11
Chapter 12 Methods for Missing Data
12.1 Missing Data Mechanisms
12.2 Methods for Missing Data
12.3 Multiple Imputation Methods
12.4 Multiple Imputation by Chained Equations
12.5 The EM Algorithm
12.6 Example in R
Exercises 12
Chapter 13 Robust Multivariate Analysis
13.1 The Need for Robust Methods
13.2 General Robust Methods
13.3 Robust Estimates of the Mean and Standard Deviation
13.4 Robust Estimates of the Covariance Matrix
13.5 Robust PCA and Regressions
13.6 Examples in R
Exercises 13
Chapter 14 Selected Topics
14.1 Likelihood Methods
14.2 Bootstrap Methods
14.3 MCMC Methods and the Gibbs Sampler
14.4 Survival Analysis
14.5 Data Science， Big Data， and Data Mining
References
Index
文摘
Chapter 1
Introduction
1.1 Goal of Statistics
A main goal of statistics is to analyze data in order to obtain useful information and make important decisions. In other words， statisticians analyze data to extract useful information from the data in a sample and draw important conclusions about the population. In modern world， many important decisions are based on information from data. With the developments of modern computers and internet， massive data are available and can be easily obtained， but important information in the data may not be easily obtained without using modern statistical methods. Therefore， statistics is becoming one of the most important subjects in the 21 century， and statistical methods are among the most widely used tools in almost every area， including banks，insurance industries， economics， finance， medicine， and engineering. As a New York Times article says (August 5， 2009， For Today's Graduate， Just One Word:Statistics): "For many different jobs in today's world， mostly what you do is data analysis (statistics)， even for jobs which seem unrelated to statistics .Many today's decisions in industry and government are based on data analysis results. Statisticians are thus in high demand ”.
Data can be collected in many ways， such as survey， internet， company records and designed experiments. Our goalis to analyze the data to extract as much information as possible and then draw some conclusions about the whole population. For example，if a new drug is found to be effective on 20 randomly selected patients (sample) based on statistical analysis， will this drug also be effective for all patients (population)?If exam scores are found to be related to students' IQ scores as well as students' attitude on 50 randomly selected students (sample)， is this also true for all students (population)? Such a generalization from sample to population is called statistical i'n，fere'n，ce. Sometimes， however， we may just wish to obtain useful information from the sample， without necessarily making inference about the population， especially if the sample is not a random and representative sample.In practice， data analysis often consists of two stages:
* exploratory data analysis.
* formal (or confirmatory) data analysis.
In exploratory data analyses， data are simply summarized using common statistics (e.g.， means， standard deviations， correlations) and are displayed using common graphical tools (e.g.， histograms， boxplots， scatterplots). In this stage， we simply present and summarize the data， without trying to generalize the conclusions ob- tained from summary statistics and graphs to the whole population. In this stage，we do not need to make any distributional assumptions for the data， i.e.， we do not need to assume that data follow certain distributions such as the normal distributions.Thus， the conclusions obtained from exploratory analysis do not depend on the va-lidity of any assumptions. Exploratory analysis can reveal important features of the data， which may lead to preliminary conclusions. Exploratory data analysis is an important step in any data analysis and should not be skipped. However，exploratory data analysis is usually followed by a formal or confirmatory analysis，which is used to confirm the preliminary conclusions from the exploratory analysis.In formal (or confirmatory) data analysis， we assume models or distributions for the data or population， estimate unknown parameters in the models or distributions，and attempt to make statisticalinference so that we may generalize the results based on the sample to the whole population. For example， we may assume that the popu-lation follows a normal distribution， and then we use data to estimate the parameters in the normal distribution (mean and variance). Note that the models or distributions are only assumptions， so they may not be true. In other words， the assumed mod-els and distributions should be checked for their validity based on the data. Since the assumed models or distributions rarely hold exactly， formal analysis results should only be viewed as approximate， and it is desirable to use different statistical models or methods to further validate the results.
In practice， data are often collected on many variables， such as age， income， and education. When we analyze data on each variable separately， the data on each vari-able are called ufn，ivariate data， and an analysis of univariate data is called un，ivariate a'n.alysis. In univariate analysis， the association or correlation between dif-ferent variables are ignored. In other words， when we analyze data on one variable in univariate analysis， we cannot borrow information from data of other variables. For example， suppose that we have data on income from a sample survey. In univariate analysis， we can compute the average income and the standard deviations. But if an income is missing or not reported， we cannot estimate it without information from other variables such as age an
| ISBN | 9787030412430,7030412435 |
|---|---|
| 出版社 | 科学出版社 |
| 作者 | 吴浪 |
| 尺寸 | 5 |