ContentsPrefaceChapter 1 Confounder and Non-confounder 11.1 Relations among Homogeneity， Collapsibility and Non-confounding 11.1.1 Basic Knowledge and Related Research 11.1.2 Non-confounding， Homogeneity and Collapsibility 31.1.3 Uniformly Non-confounding 31.2 Uniformly Non-confounding of Causal Distribution Effects 71.2.1 Introduction 71.2.2 Concepts of Non-confounding over Multiple Covariates 81.2.3 Condition for Non-confounding over Multiple Covariates 101.2.4 Discussion and Future Plans 151.3 How to Detect Multiple Confounders 151.3.1 Introduction 151.3.2 Concepts 171.3.3 Uniform Non-confounding over Multiple Covariates 191.3.4 Non-confounding in Subpopulations 251.3.5 Some Examples 26References 28Chapter 2 Whether to Adjust for a Non-confounder 302.1 Whether to Adjust for a Non-confounder 302.1.1 Related Research and Controversy 302.1.2 Confounding Bias， Confounder and Standardization 312.1.3 Estimates of Hypothetical Proportion 322.1.4 Expectation and Variances of Estimates 342.1.5 Proofs of Theorems 362.2 The Estimation of Log Relative Risk with Non-confounder 432.2.1 Introduction of Basic Knowledge 432.2.2 Asymptotical Estimators 452.2.3 Counterexamples of Theorem 2.2.3 502.2.4 Conclusion and Discussion 50References 51Chapter 3 Binomial Parameter Estimation with Missing Data 543.1 Binomial Proportion Estimation with Missing Data 543.1.1 Introduction 543.1.2 The Model and Notations 553.1.3 Asymptotic Variance Estimation 563.1.4 Simulation Results 613.1.5 Concluding Remarks 623.2 Using Auxiliary Data for Binomial Parameter Estimation with Missing Data 623.2.1 Basic Knowledge and Related Research 623.2.2 Concepts and Some Notations 643.2.3 Maximum Likelihood Estimator of Variance 653.2.4 Simulation Study 703.2.5 Application of Real Data 713.2.6 Concluding Remarks 72References 72Chapter 4 A Bayesian Approach and its Applications 744.1 Forecasting of COVID-19 Onset Cases 744.1.1 Related Works 744.1.2 Construction of the Model 764.1.3 Results of Statistical Analysis 804.1.4 Conclusion and Discussion 824.2 A Bayesian Approach to Forecast Chinese Foodborne Diseases 834.2.1 Related Basic Knowledge 834.2.2 Data Material and Statistical Analysis 844.2.3 Main Results 904.2.4 Conclusion and Discussion 95References 96Chapter 5 Statistical Analysis and Inference of Traffic Load 1005.1 Traffic Load Prediction Based on Shrinkage Estimation 1005.1.1 Introduction 1005.1.2 Data Source 1015.1.3 Model Construction and Parameters Prediction 1045.1.4 Discussion 1105.2 Spatial-temporal Analysis of Traffic Load in Mobile Cellular Network 1105.2.1 Introduction 1105.2.2 Model Construction 1115.2.3 Conclusion and Discussion 119References 119Chapter 6 Identification and Authentication of Radio Frequency Signal 1216.1 Identification and Authentication for Wireless Transmission Security 1216.1.1 Introduction 1216.1.2 Experimental Process 1236.1.3 Statistic Fingerprint Generation 1266.1.4 Classification Methods 1296.1.5 Results and Discussion 1326.1.6 Conclusion 1356.2 Radio Frequency Signal Identification Using Transfer Learning Based on LSTM 1366.2.1 Overview of Basic Knowledge 1366.2.2 Related Works 1386.2.3 Methods 1396.2.4 Experimental Process 1426.2.5 Conclusion 147References 148
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