LITMIR.BIZ

Скачать книгу

Linear Regression 3 Interaction‐Effect Selection for High‐Dimensional Data 4 Model Selection in High‐Dimensional Nonparametric Models 5 Concluding Remarks References 18 Sampling Local Scale Parameters in High-Dimensional Regression Models 1 Introduction 2 A Blocked Gibbs Sampler for the Horseshoe 3 Sampling

4 Sampling

5 Appendix: A. Newton–Raphson Steps for the Inverse‐cdf Sampler for

Acknowledgment References Note 19 Factor Modeling for High-Dimensional Time Series 1 Introduction 2 Identifiability 3 Estimation of High‐Dimensional Factor Model 4 Determining the Number of Factors Acknowledgment References

      10  Part V: Quantitative Visualization 20 Visual Communication of Data: It Is Not a Programming Problem, It Is Viewer Perception 1 Introduction 2 Case Studies Part 1 3 Let StAR Be Your Guide 4 Case Studies Part 2: Using StAR Principles to Develop Better Graphics 5 Ask Colleagues Their Opinion 6 Case Studies: Part 3 7 Iterate 8 Final Thoughts Notes References 21 Uncertainty Visualization 1 Introduction 2 Uncertainty Visualization Theories 3 General Discussion References 22 Big Data Visualization 1 Introduction 2 Architecture for Big Data Analytics 3 Filtering 4 Aggregating 5 Analyzing 6 Big Data Graphics 7 Conclusion References 23 Visualization‐Assisted Statistical Learning 1 Introduction 2 Better Visualizations with Seriation 3 Visualizing Machine Learning Fits 4 Condvis2 Case Studies 5 Discussion References 24 Functional Data Visualization 1 Introduction 2 Univariate Functional Data Visualization 3 Multivariate Functional Data Visualization 4 Conclusions Acknowledgment References

      11  Part VI: Numerical Approximation and Optimization 25 Gradient‐Based Optimizers for Statistics and Machine Learning 1 Introduction 2 Convex Versus Nonconvex Optimization 3 Gradient Descent 4 Proximal Gradient Descent: Handling Nondifferentiable Regularization 5 Stochastic Gradient Descent References 26 Alternating Minimization Algorithms 1 Introduction 2 Coordinate Descent 3 EM as Alternating Minimization 4 Matrix Approximation Algorithms 5 Conclusion References 27

Скачать книгу