Perspectives on Big Data Analysis : Methodologies and Applications.

Cover -- Title page -- Contents -- Preface -- Principal Component Analysis (PCA) for high-dimensional data. PCA is dead. Long live PCA -- 1. Introduction -- 2. Association regression models based on PCA -- 3. Dual eigenanalysis and models for stratified populations -- 4. Association test statistics -- 5. Simulations -- References -- Solving a System of High-Dimensional Equations by MCMC -- 1. Introduction: Background -- 2. The underlying equations -- 3. Reparameterization using a latent variable -- 4. The statistical model and its solution -- 5. Extending the approach -- 6. Summary and conclusions -- References -- A slice sampler for the hierarchical Poisson/Gamma random field model -- 1. Introduction -- 2. Hierarchical Poisson/gamma random fields -- 3. The algorithm -- 4. Examples -- 5. Meta-analysis of functional neuroimaging data -- 6. Discussion -- References -- A new penalized quasi-likelihood approach for estimating the number of states in a hidden Markov model -- 1. Introduction -- 2. Hidden Markov models -- 3. The new method for order estimation: MSCAD_{ } -- 4. Asymptotic study -- 5. Simulation studies -- 6. Empirical illustrations -- 7. Discussion and conclusion -- Appendix A. Proofs -- Appendix B. Simulations -- References -- Efficient adaptive estimation strategies in high-dimensional partially linear regression models -- 1. Introduction -- 2. High-dimensional shrinkage strategy -- 3. Asymptotic analysis -- 4. Numerical studies -- 5. Real data analysis -- 6. Concluding remarks and future outlook -- Appendix A. -- References -- Geometry and properties of generalized ridge regression in high dimensions -- 1. Introduction -- 2. Geometry and properties of the GRR estimator when ≥ -- 3. Implications for GRR when ≥ -- 4. Discussion -- Appendix A. Proofs -- References -- Multiple testing for high-dimensional data -- 1. Introduction.

2. High-dimensional efficient score test -- 3. Implementation -- 4. Simulation study -- 5. Applications -- 6. Discussion -- References -- On multiple contrast tests and simultaneous confidence intervals in high-dimensional repeated measures designs -- 1. Introduction -- 2. Statistical model, hypotheses and test statistics -- 3. Inference for \vT in a high-dimensional case -- 4. Simulations -- 5. Example -- 6. Discussion -- Appendix A. Proof of Theorem 3.1 -- References -- Data-driven smoothing can preserve good asymptotic properties -- 1. Introduction -- 2. The data-driven method -- 3. Theoretical properties -- 4. Numerical experiments -- 5. Conclusion -- Appendix A. -- References -- Variable selection for ultra-high-dimensional logistic models -- 1. Introduction -- 2. High-dimensional SCAD -- 3. Simulation studies -- 4. Real data example -- 5. Discussion -- Appendix A. -- References -- Shrinkage estimation and selection for a logistic regression model -- 1. Introduction -- 2. Estimation strategies -- 3. Asymptotic results and comparison -- 4. Simulation results -- 5. Example: low birth weight data -- 6. Discussion and conclusion -- Appendix A. Proof of Theorems 3.2 and 3.3 -- References -- Manifold unfolding by Isometric Patch Alignment with an application in protein structure determination -- 1. Introduction -- 2. The proposed method -- 3. Experiments -- 4. Conclusion -- References -- Back Cover.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.