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New Advances in Statistics and Data Science.

By: Chen, Ding-Geng.
Contributor(s): Jin, Zhezhen | Li, Gang | Li, Yi | Liu, Aiyi | Zhao, Yichuan.
Material type: TextTextSeries: eBooks on Demand.ICSA Book Series in Statistics Ser: Publisher: Cham : Springer, 2018Copyright date: ©2018Description: 1 online resource (355 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783319694160.Subject(s): Big data | StatisticsGenre/Form: Electronic books.Additional physical formats: Print version:: New Advances in Statistics and Data ScienceDDC classification: 005.7 LOC classification: QA276-280HF5548.125-Online resources: Click here to view this ebook.
Contents:
Intro -- Preface -- Part I: Review and Theoretical Framework in Data Science (Chapters 1 -5) -- Part II: Complex and Big Data Analysis (Chapters 6 -10) -- Part III: Clinical Trials, Statistical Shape Analysis, and Applications (Chapters 11 -14) -- Part IV: Statistical Modeling and Data Analysis (Chapters 15 -19) -- Contents -- Contributors -- About the Editors -- List of Chapter Reviewers -- Part I Review and Theoretical Framework in Data Science -- Statistical Distances and Their Role in Robustness -- 1 Introduction -- 2 The Discrete Setting -- 3 Chi-Squared Distance Measures -- 3.1 Loss Function Interpretation -- 3.2 Loss Analysis of Pearson and Neyman Chi-Squared Distances -- 3.3 Metric Properties of the Symmetric Chi-Squared Distance -- 3.4 Locally Quadratic Distances -- 4 The Continuous Setting -- 4.1 Desired Features -- 4.2 The L2- .4 -Distance -- 4.3 The Kolmogorov-Smirnov Distance -- 4.4 Exactly Quadratic Distances -- 5 Discussion -- References -- The Out-of-Source Error in Multi-Source Cross Validation-Type Procedures -- 1 Introduction -- 2 Literature Review and Notation -- 3 Framework -- 4 The OOS Error Estimation -- 4.1 Estimating the OOS Error -- 4.2 Bias and Variance of μ0 -- 090d"0362 μμ0 -- 090d"0362 μμ0 -- 090d"0362 μμ0 -- 090d"0362 μos- .4 -- 4.3 On Variance Estimation -- 5 Simulation Study -- 6 Discussion -- Appendix 1: Some Useful Relations -- Appendix 2: On Moments of Bivariate Normal Distribution -- Appendix 3: Proofs -- References -- Meta-Analysis for Rare Events As Binary Outcomes -- 1 Introduction -- 2 Methods -- 2.1 Non-Parametric Meta-Analysis -- 2.1.1 Mantel-Haenszel Method -- 2.1.2 Peto Odds Ratio -- 2.1.3 Exact Method of Constructing Confidence Intervals for Risk Differences (Tian et al. 2009) -- 2.2 Parametric Meta-Analysis -- 2.2.1 Random-Effects Regression Model -- 2.2.2 Random-Effects Beta-Binomial Model.
2.2.3 Random-Effects Poisson Model -- 2.3 Parametric Bootstrap Resampling Meta-Analysis -- 3 Case Studies -- 3.1 A Rosiglitazone Meta-Analysis Study -- 3.2 A Transplant Extrapolation Study with Everolimus -- 4 Summary -- Appendix: Data for the rosiglitazone meta-analysis study from Nissen and Wolski (2007) -- References -- New Challenges and Strategies in Robust Optimal Design for Multicategory Logit Modelling -- 1 Introduction -- 2 Quantal Dose-Response Modelling -- 3 Confidence Regions and Intervals -- 4 Optimal Design Theory -- 5 Near-Optimal Robust Design Strategies -- 6 Discussion -- References -- Testing of Multivariate Spline Growth Model -- 1 Introduction -- 2 Modeling Growth with Smooth Functions -- 3 Testing of Mean Curves -- 3.1 Spline Approximation -- 3.2 Constructing a Test for Mean Spline Curves -- 4 Multivariate Spline Growth Curve Model -- 5 Computational Example: Modeling in Behavioral Cardiology -- References -- Part II Complex and Big Data Analysis -- Uncertainty Quantification Using the Nearest Neighbor GaussianProcess -- 1 Introduction -- 2 Methods -- 2.1 Modeling with Gaussian Process -- 2.2 Bayesian Inference and Computational Considerations -- 3 Simulation Experiments -- 4 Application: Uncertainty Quantification for Surface Data -- 5 Conclusions and Discussion -- References -- Tuning Parameter Selection in the LASSO with UnspecifiedPropensity -- 1 Introduction -- 2 Model and Method -- 3 Tuning Parameter Selection in the LASSO -- 3.1 Multifold Cross Validation (CV) -- 3.2 Bayesian Information Criterion (BIC) -- 3.3 Variable Selection Stability (VSS) -- 3.4 Estimation Stability (ESCV) -- 4 Simulation Studies -- 5 Melanoma Study -- 6 Discussion -- References -- Adaptive Filtering Increases Power to Detect Differentially Expressed Genes -- 1 Introduction -- 2 Existing Filtering Methods -- 3 Proposed Method.
4 A Data-Based Simulation Study -- 5 Conclusion -- References -- Estimating Parameters in Complex Systems with Functional Outputs: A Wavelet-Based Approximate Bayesian Computation Approach -- 1 Introduction -- 2 A Motivating Example: The Foliage-Echo Simulation System -- 3 Wavelet-Based Approximate Bayesian Computation -- 3.1 Review of Approximate Bayesian Computation -- 3.2 Wavelet Representation and Compression of Functional Data -- 3.3 A Gaussian Process Surrogate for the Simulator -- 3.4 Control the Uncertainty of Decision-Making in wABC Using GPS -- 4 The Algorithm and Parameter Settings -- 5 The Analysis of Simulated Foliage-Echo Data -- 6 Discussion -- Appendix: More Details of the Foliage-Echo Simulator -- References -- A Maximum Likelihood Approach for Non-invasive Cancer Diagnosis Using Methylation Profiling of Cell-Free DNA from Blood -- 1 Introduction -- 2 Methods -- 2.1 Model the Methylation Probabilities -- 2.2 Estimate the Composition of Tumor-Derived cfDNA Using Methylation Data -- 2.3 Simulate the Methylation Sequencing Data of Plasma cfDNA Samples -- 2.4 Cancer Prediction Using Estimated Fraction of Tumor-Derived cfDNA and Evaluation Criteria -- 2.5 Applications to Real Data -- 3 Results -- 3.1 Estimation Accuracy Increases with Sequencing Depth and Fraction of Tumor-Derived cfDNA in Simulation Data -- 3.2 Estimated Fraction of Tumor-Derived cfDNA in Real Blood Samples Can Predict Normal from Liver Cancer Patients -- 3.3 The Fractions of Tumor-Derived cfDNA in the Blood of Liver Cancer Patients Are Significantly Decreased After Surgery -- 4 Discussion and Conclusions -- References -- Part III Clinical Trials, Statistical Shape Analysis and Applications -- A Simple and Efficient Statistical Approach for Designing an Early Phase II Clinical Trial: Ordinal Linear Contrast Test -- 1 Introduction -- 2 Notation and Assumptions.
2.1 Model Description -- 2.2 Monotonicity -- 2.3 Family-Wise Error Rate -- 3 Statistical Methods -- 3.1 Ordinal Linear Contrast Test -- 3.2 MCP-Mod -- 3.3 ANOVA F Test -- 3.4 MaxT Test -- 4 Dose Ranging -- 5 Method Comparisons -- 5.1 OLCT Approach -- 5.2 MCP-Mod Approach -- 5.3 ANOVA Approach -- 5.4 MaxT Approach -- 5.5 Comparisons -- 5.6 When to Use MCP-Mod and When Not? -- 5.7 When to Use OLCT and When Not? -- 5.8 Limitations of ANOVA F Test -- 6 Discussion -- References -- Landmark-Constrained Statistical Shape Analysis of Elastic Curves and Surfaces -- 1 Introduction -- 2 Landmark-Constrained Shape Analysis -- 2.1 Unconstrained Representation Spaces of Curves and Surfaces -- 2.2 Landmark-Constrained Shape Space for Curves -- 2.3 Landmark-Constrained Shape Space for Surfaces -- 2.4 Motivating Examples -- 2.5 Additional Examples -- 3 Statistical Analysis of Landmark-Constrained Shapes -- 3.1 Sample Averaging -- 3.1.1 Examples -- 3.2 Summarization of Variability -- 3.2.1 Examples -- 4 Summary -- References -- Phylogeny-Based Kernels with Application to Microbiome Association Studies -- 1 Introduction -- 2 Methods -- 2.1 Phylogeny-Induced Correlation Structure Among OTUs -- 2.2 A Phylogeny-Based Kernel for Microbiome Data -- 2.3 Kernel-Machine (KM) Association Test -- 2.3.1 Single Kernel-Based KM Association Test -- 2.3.2 Multiple Kernel-Based Optimal KM Association Test -- 3 Simulation Studies -- 3.1 Simulation Details -- 3.2 Results on Simulated Data -- 4 Application to a Real Data Set -- 5 Discussion -- References -- Accounting for Differential Error in Time-to-Event Analyses Using Imperfect Electronic Health Record-Derived Endpoints -- 1 Introduction -- 2 Methods -- 2.1 Definitions and Notation -- 2.2 Discrete Proportional Hazards Model -- 2.3 Adjustment for Error in Event Times -- 2.4 Incorporating Person-Level Validation Data.
2.5 Differential Error in Event Times -- 2.6 Simulation Study Design -- 3 Results -- 3.1 Non-differential Error in Dates -- 3.2 Differential Error in Dates -- 4 Discussion -- References -- Part IV Statistical Modeling and Data Analysis -- Modeling Inter-Trade Durations in the Limit Order Market -- 1 Introduction -- 2 Empirical Facts -- 3 Model and Estimation -- 3.1 Model Specification -- 3.2 Maximum Likelihood -- 4 Empirical Analysis -- 5 Conclusion -- References -- Assessment of Drug Interactions with Repeated Measurements -- 1 Introduction -- 2 Median Effect Model for Drug Combination Effects -- 2.1 Median Effect Model -- 2.2 Ray Design -- 2.3 Loewe Additivity -- 2.4 Drug Combination Effects with Repeated Measurements -- 3 Confidence Interval Estimation at the Observed Combination -- 4 Confidence Bound for Interaction Index on a Fixed Ray -- 5 Simulation Study -- 6 Application -- 7 Discussion -- References -- Statistical Indices for Risk Tracking in Longitudinal Studies -- 1 Introduction -- 2 Rank-Based Tracking Indices -- 2.1 Rank-Tracking Probabilities -- 2.2 Rank-Tracking Probability Ratios -- 2.3 Mean-Integrated RTPs and RTPRs -- 3 Estimation and Inference Methods -- 3.1 Nonparametric Mixed Models and Prediction -- 3.2 Estimation of Tracking Indices -- 3.3 Bootstrap Confidence Intervals -- 4 Application to the NGHS Data -- 4.1 Rank-Tracking for BMI -- 4.2 Rank-Tracking for SBP -- 5 Simulation -- 6 Discussion -- References -- Statistical Analysis of Labor Market Integration: A Mixture Regression Approach -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Multivariate Binary Mixture -- 3 Analysis -- 3.1 Normal Life-Course -- 3.2 Weak Labor Market Integration -- 4 Concluding Remarks -- Appendix -- References -- Bias Correction in Age-Period-Cohort Models Using Eigen Analysis -- 1 Introduction.
2 Age-Period-Cohort Model and the Identification Problem.
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Intro -- Preface -- Part I: Review and Theoretical Framework in Data Science (Chapters 1 -5) -- Part II: Complex and Big Data Analysis (Chapters 6 -10) -- Part III: Clinical Trials, Statistical Shape Analysis, and Applications (Chapters 11 -14) -- Part IV: Statistical Modeling and Data Analysis (Chapters 15 -19) -- Contents -- Contributors -- About the Editors -- List of Chapter Reviewers -- Part I Review and Theoretical Framework in Data Science -- Statistical Distances and Their Role in Robustness -- 1 Introduction -- 2 The Discrete Setting -- 3 Chi-Squared Distance Measures -- 3.1 Loss Function Interpretation -- 3.2 Loss Analysis of Pearson and Neyman Chi-Squared Distances -- 3.3 Metric Properties of the Symmetric Chi-Squared Distance -- 3.4 Locally Quadratic Distances -- 4 The Continuous Setting -- 4.1 Desired Features -- 4.2 The L2- .4 -Distance -- 4.3 The Kolmogorov-Smirnov Distance -- 4.4 Exactly Quadratic Distances -- 5 Discussion -- References -- The Out-of-Source Error in Multi-Source Cross Validation-Type Procedures -- 1 Introduction -- 2 Literature Review and Notation -- 3 Framework -- 4 The OOS Error Estimation -- 4.1 Estimating the OOS Error -- 4.2 Bias and Variance of μ0 -- 090d"0362 μμ0 -- 090d"0362 μμ0 -- 090d"0362 μμ0 -- 090d"0362 μos- .4 -- 4.3 On Variance Estimation -- 5 Simulation Study -- 6 Discussion -- Appendix 1: Some Useful Relations -- Appendix 2: On Moments of Bivariate Normal Distribution -- Appendix 3: Proofs -- References -- Meta-Analysis for Rare Events As Binary Outcomes -- 1 Introduction -- 2 Methods -- 2.1 Non-Parametric Meta-Analysis -- 2.1.1 Mantel-Haenszel Method -- 2.1.2 Peto Odds Ratio -- 2.1.3 Exact Method of Constructing Confidence Intervals for Risk Differences (Tian et al. 2009) -- 2.2 Parametric Meta-Analysis -- 2.2.1 Random-Effects Regression Model -- 2.2.2 Random-Effects Beta-Binomial Model.

2.2.3 Random-Effects Poisson Model -- 2.3 Parametric Bootstrap Resampling Meta-Analysis -- 3 Case Studies -- 3.1 A Rosiglitazone Meta-Analysis Study -- 3.2 A Transplant Extrapolation Study with Everolimus -- 4 Summary -- Appendix: Data for the rosiglitazone meta-analysis study from Nissen and Wolski (2007) -- References -- New Challenges and Strategies in Robust Optimal Design for Multicategory Logit Modelling -- 1 Introduction -- 2 Quantal Dose-Response Modelling -- 3 Confidence Regions and Intervals -- 4 Optimal Design Theory -- 5 Near-Optimal Robust Design Strategies -- 6 Discussion -- References -- Testing of Multivariate Spline Growth Model -- 1 Introduction -- 2 Modeling Growth with Smooth Functions -- 3 Testing of Mean Curves -- 3.1 Spline Approximation -- 3.2 Constructing a Test for Mean Spline Curves -- 4 Multivariate Spline Growth Curve Model -- 5 Computational Example: Modeling in Behavioral Cardiology -- References -- Part II Complex and Big Data Analysis -- Uncertainty Quantification Using the Nearest Neighbor GaussianProcess -- 1 Introduction -- 2 Methods -- 2.1 Modeling with Gaussian Process -- 2.2 Bayesian Inference and Computational Considerations -- 3 Simulation Experiments -- 4 Application: Uncertainty Quantification for Surface Data -- 5 Conclusions and Discussion -- References -- Tuning Parameter Selection in the LASSO with UnspecifiedPropensity -- 1 Introduction -- 2 Model and Method -- 3 Tuning Parameter Selection in the LASSO -- 3.1 Multifold Cross Validation (CV) -- 3.2 Bayesian Information Criterion (BIC) -- 3.3 Variable Selection Stability (VSS) -- 3.4 Estimation Stability (ESCV) -- 4 Simulation Studies -- 5 Melanoma Study -- 6 Discussion -- References -- Adaptive Filtering Increases Power to Detect Differentially Expressed Genes -- 1 Introduction -- 2 Existing Filtering Methods -- 3 Proposed Method.

4 A Data-Based Simulation Study -- 5 Conclusion -- References -- Estimating Parameters in Complex Systems with Functional Outputs: A Wavelet-Based Approximate Bayesian Computation Approach -- 1 Introduction -- 2 A Motivating Example: The Foliage-Echo Simulation System -- 3 Wavelet-Based Approximate Bayesian Computation -- 3.1 Review of Approximate Bayesian Computation -- 3.2 Wavelet Representation and Compression of Functional Data -- 3.3 A Gaussian Process Surrogate for the Simulator -- 3.4 Control the Uncertainty of Decision-Making in wABC Using GPS -- 4 The Algorithm and Parameter Settings -- 5 The Analysis of Simulated Foliage-Echo Data -- 6 Discussion -- Appendix: More Details of the Foliage-Echo Simulator -- References -- A Maximum Likelihood Approach for Non-invasive Cancer Diagnosis Using Methylation Profiling of Cell-Free DNA from Blood -- 1 Introduction -- 2 Methods -- 2.1 Model the Methylation Probabilities -- 2.2 Estimate the Composition of Tumor-Derived cfDNA Using Methylation Data -- 2.3 Simulate the Methylation Sequencing Data of Plasma cfDNA Samples -- 2.4 Cancer Prediction Using Estimated Fraction of Tumor-Derived cfDNA and Evaluation Criteria -- 2.5 Applications to Real Data -- 3 Results -- 3.1 Estimation Accuracy Increases with Sequencing Depth and Fraction of Tumor-Derived cfDNA in Simulation Data -- 3.2 Estimated Fraction of Tumor-Derived cfDNA in Real Blood Samples Can Predict Normal from Liver Cancer Patients -- 3.3 The Fractions of Tumor-Derived cfDNA in the Blood of Liver Cancer Patients Are Significantly Decreased After Surgery -- 4 Discussion and Conclusions -- References -- Part III Clinical Trials, Statistical Shape Analysis and Applications -- A Simple and Efficient Statistical Approach for Designing an Early Phase II Clinical Trial: Ordinal Linear Contrast Test -- 1 Introduction -- 2 Notation and Assumptions.

2.1 Model Description -- 2.2 Monotonicity -- 2.3 Family-Wise Error Rate -- 3 Statistical Methods -- 3.1 Ordinal Linear Contrast Test -- 3.2 MCP-Mod -- 3.3 ANOVA F Test -- 3.4 MaxT Test -- 4 Dose Ranging -- 5 Method Comparisons -- 5.1 OLCT Approach -- 5.2 MCP-Mod Approach -- 5.3 ANOVA Approach -- 5.4 MaxT Approach -- 5.5 Comparisons -- 5.6 When to Use MCP-Mod and When Not? -- 5.7 When to Use OLCT and When Not? -- 5.8 Limitations of ANOVA F Test -- 6 Discussion -- References -- Landmark-Constrained Statistical Shape Analysis of Elastic Curves and Surfaces -- 1 Introduction -- 2 Landmark-Constrained Shape Analysis -- 2.1 Unconstrained Representation Spaces of Curves and Surfaces -- 2.2 Landmark-Constrained Shape Space for Curves -- 2.3 Landmark-Constrained Shape Space for Surfaces -- 2.4 Motivating Examples -- 2.5 Additional Examples -- 3 Statistical Analysis of Landmark-Constrained Shapes -- 3.1 Sample Averaging -- 3.1.1 Examples -- 3.2 Summarization of Variability -- 3.2.1 Examples -- 4 Summary -- References -- Phylogeny-Based Kernels with Application to Microbiome Association Studies -- 1 Introduction -- 2 Methods -- 2.1 Phylogeny-Induced Correlation Structure Among OTUs -- 2.2 A Phylogeny-Based Kernel for Microbiome Data -- 2.3 Kernel-Machine (KM) Association Test -- 2.3.1 Single Kernel-Based KM Association Test -- 2.3.2 Multiple Kernel-Based Optimal KM Association Test -- 3 Simulation Studies -- 3.1 Simulation Details -- 3.2 Results on Simulated Data -- 4 Application to a Real Data Set -- 5 Discussion -- References -- Accounting for Differential Error in Time-to-Event Analyses Using Imperfect Electronic Health Record-Derived Endpoints -- 1 Introduction -- 2 Methods -- 2.1 Definitions and Notation -- 2.2 Discrete Proportional Hazards Model -- 2.3 Adjustment for Error in Event Times -- 2.4 Incorporating Person-Level Validation Data.

2.5 Differential Error in Event Times -- 2.6 Simulation Study Design -- 3 Results -- 3.1 Non-differential Error in Dates -- 3.2 Differential Error in Dates -- 4 Discussion -- References -- Part IV Statistical Modeling and Data Analysis -- Modeling Inter-Trade Durations in the Limit Order Market -- 1 Introduction -- 2 Empirical Facts -- 3 Model and Estimation -- 3.1 Model Specification -- 3.2 Maximum Likelihood -- 4 Empirical Analysis -- 5 Conclusion -- References -- Assessment of Drug Interactions with Repeated Measurements -- 1 Introduction -- 2 Median Effect Model for Drug Combination Effects -- 2.1 Median Effect Model -- 2.2 Ray Design -- 2.3 Loewe Additivity -- 2.4 Drug Combination Effects with Repeated Measurements -- 3 Confidence Interval Estimation at the Observed Combination -- 4 Confidence Bound for Interaction Index on a Fixed Ray -- 5 Simulation Study -- 6 Application -- 7 Discussion -- References -- Statistical Indices for Risk Tracking in Longitudinal Studies -- 1 Introduction -- 2 Rank-Based Tracking Indices -- 2.1 Rank-Tracking Probabilities -- 2.2 Rank-Tracking Probability Ratios -- 2.3 Mean-Integrated RTPs and RTPRs -- 3 Estimation and Inference Methods -- 3.1 Nonparametric Mixed Models and Prediction -- 3.2 Estimation of Tracking Indices -- 3.3 Bootstrap Confidence Intervals -- 4 Application to the NGHS Data -- 4.1 Rank-Tracking for BMI -- 4.2 Rank-Tracking for SBP -- 5 Simulation -- 6 Discussion -- References -- Statistical Analysis of Labor Market Integration: A Mixture Regression Approach -- 1 Introduction -- 2 Methods -- 2.1 Data -- 2.2 Multivariate Binary Mixture -- 3 Analysis -- 3.1 Normal Life-Course -- 3.2 Weak Labor Market Integration -- 4 Concluding Remarks -- Appendix -- References -- Bias Correction in Age-Period-Cohort Models Using Eigen Analysis -- 1 Introduction.

2 Age-Period-Cohort Model and the Identification Problem.

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