因果推理和贝叶斯统计的理论和应用（英文版）

　　《因果推断和贝叶斯统计的理论及应用（英文版）》主要研究了因果推断中多混杂因素的判别关于非混杂因素进行标准化对估计精度的影响在不可忽略缺失机制下不完全数据的统计分析以及不可忽略缺失机制下不完全数据的二项分布效应的估计，以及因果断理论在应用统计的*新研究等问题。

暂缺《因果推理和贝叶斯统计的理论和应用（英文版）》作者简介

Contents
Preface
Chapter 1 Confounder and Non-confounder 1
1.1 Relations among Homogeneity， Collapsibility and Non-confounding 1
1.1.1 Basic Knowledge and Related Research 1
1.1.2 Non-confounding， Homogeneity and Collapsibility 3
1.1.3 Uniformly Non-confounding 3
1.2 Uniformly Non-confounding of Causal Distribution Effects 7
1.2.1 Introduction 7
1.2.2 Concepts of Non-confounding over Multiple Covariates 8
1.2.3 Condition for Non-confounding over Multiple Covariates 10
1.2.4 Discussion and Future Plans 15
1.3 How to Detect Multiple Confounders 15
1.3.1 Introduction 15
1.3.2 Concepts 17
1.3.3 Uniform Non-confounding over Multiple Covariates 19
1.3.4 Non-confounding in Subpopulations 25
1.3.5 Some Examples 26
References 28
Chapter 2 Whether to Adjust for a Non-confounder 30
2.1 Whether to Adjust for a Non-confounder 30
2.1.1 Related Research and Controversy 30
2.1.2 Confounding Bias， Confounder and Standardization 31
2.1.3 Estimates of Hypothetical Proportion 32
2.1.4 Expectation and Variances of Estimates 34
2.1.5 Proofs of Theorems 36
2.2 The Estimation of Log Relative Risk with Non-confounder 43
2.2.1 Introduction of Basic Knowledge 43
2.2.2 Asymptotical Estimators 45
2.2.3 Counterexamples of Theorem 2.2.3 50
2.2.4 Conclusion and Discussion 50
References 51
Chapter 3 Binomial Parameter Estimation with Missing Data 54
3.1 Binomial Proportion Estimation with Missing Data 54
3.1.1 Introduction 54
3.1.2 The Model and Notations 55
3.1.3 Asymptotic Variance Estimation 56
3.1.4 Simulation Results 61
3.1.5 Concluding Remarks 62
3.2 Using Auxiliary Data for Binomial Parameter Estimation with Missing Data 62
3.2.1 Basic Knowledge and Related Research 62
3.2.2 Concepts and Some Notations 64
3.2.3 Maximum Likelihood Estimator of Variance 65
3.2.4 Simulation Study 70
3.2.5 Application of Real Data 71
3.2.6 Concluding Remarks 72
References 72
Chapter 4 A Bayesian Approach and its Applications 74
4.1 Forecasting of COVID-19 Onset Cases 74
4.1.1 Related Works 74
4.1.2 Construction of the Model 76
4.1.3 Results of Statistical Analysis 80
4.1.4 Conclusion and Discussion 82
4.2 A Bayesian Approach to Forecast Chinese Foodborne Diseases 83
4.2.1 Related Basic Knowledge 83
4.2.2 Data Material and Statistical Analysis 84
4.2.3 Main Results 90
4.2.4 Conclusion and Discussion 95
References 96
Chapter 5 Statistical Analysis and Inference of Traffic Load 100
5.1 Traffic Load Prediction Based on Shrinkage Estimation 100
5.1.1 Introduction 100
5.1.2 Data Source 101
5.1.3 Model Construction and Parameters Prediction 104
5.1.4 Discussion 110
5.2 Spatial-temporal Analysis of Traffic Load in Mobile Cellular Network 110
5.2.1 Introduction 110
5.2.2 Model Construction 111
5.2.3 Conclusion and Discussion 119
References 119
Chapter 6 Identification and Authentication of Radio Frequency Signal 121
6.1 Identification and Authentication for Wireless Transmission Security 121
6.1.1 Introduction 121
6.1.2 Experimental Process 123
6.1.3 Statistic Fingerprint Generation 126
6.1.4 Classification Methods 129
6.1.5 Results and Discussion 132
6.1.6 Conclusion 135
6.2 Radio Frequency Signal Identification Using Transfer Learning Based on LSTM 136
6.2.1 Overview of Basic Knowledge 136
6.2.2 Related Works 138
6.2.3 Methods 139
6.2.4 Experimental Process 142
6.2.5 Conclusion 147
References 148