Geometry Driven Statistics.

Intro -- Title Page -- Preface -- List of Contributors -- Part I: Kanti Mardia -- Chapter 1: A Conversation with Kanti Mardia -- 1.1 Family background -- 1.2 School days -- 1.3 College life -- 1.4 Ismail Yusuf College-University of Bombay -- 1.5 University of Bombay -- 1.6 A taste of the real world -- 1.7 Changes in the air -- 1.8 University of Rajasthan -- 1.9 Commonwealth scholarship to England -- 1.10 University of Newcastle -- 1.11 University of Hull -- 1.12 Book writing at the University of Hull -- 1.13 Directional data analysis -- 1.14 Chair Professorship of Applied Statistics, University of Leeds -- 1.15 Leeds annual workshops and conferences -- 1.16 High profile research areas -- 1.17 Center of Medical Imaging Research (CoMIR) -- 1.18 Visiting other places -- 1.19 Collaborators, colleagues and personalities -- 1.20 Logic, statistics and Jain religion -- 1.21 Many hobbies -- 1.22 Immediate family -- 1.23 Retirement 2000 -- Acknowledgments -- References -- Chapter 2: A Conversation with Kanti Mardia: Part II -- 2.1 Introduction -- 2.2 Leeds, Oxford, and other affiliations -- 2.3 Book writing: revising and new ones -- 2.4 Research: bioinformatics and protein structure -- 2.5 Research: not necessarily linked directly with bioinformatics -- 2.6 Organizing centers and conferences -- 2.7 Memorable conference trips -- 2.8 A select group of special colleagues -- 2.9 High honors -- 2.10 Statistical science: thoughts and predictions -- 2.11 Immediate family -- 2.12 Jain thinking -- 2.13 What the future may hold -- Acknowledgment -- References -- Chapter 3: Selected publications -- 1. Monographs -- 2. Edited Volumes -- 3. Journal Research Papers -- 4. Articles in Edited Volumes (other than edited by Mardia) -- Part II: Directional Data Analysis -- Chapter 4: Some advances in constrained inference for ordered circular parameters in oscillatory systems.

4.1 Introduction -- 4.2 Oscillatory data and the problems of interest -- 4.3 Estimation of angular parameters under order constraint -- 4.4 Inferences under circular restrictions in von Mises models -- 4.5 The estimation of a common circular order from multiple experiments -- 4.6 Application: analysis of cell cycle gene expression data -- 4.7 Concluding remarks and future research -- Acknowledgment -- References -- Chapter 5: Parametric circular-circular regression and diagnostic analysis -- 5.1 Introduction -- 5.2 Review of models -- 5.3 Parameter estimation and inference -- 5.4 Diagnostic analysis -- 5.5 Examples -- 5.6 Discussion -- References -- Chapter 6: On two-sample tests for circular data based on spacing-frequencies -- 6.1 Introduction -- 6.2 Spacing-frequencies tests for circular data -- 6.3 Rao's spacing-frequencies test for circular data -- 6.4 Monte Carlo power comparisons -- Acknowledgments -- References -- Chapter 7: Barycentres and hurricane trajectories -- 7.1 Introduction -- 7.2 Barycentres -- 7.3 Hurricanes -- 7.4 Using k-means and non-parametric statistics -- 7.5 Results -- 7.6 Conclusion -- Acknowledgment -- References -- Part III: Shape Analysis -- Chapter 8: Beyond Procrustes: a proposal to save morphometrics for biology -- 8.1 Introduction -- 8.2 Analytic preliminaries -- 8.3 The core maneuver -- 8.4 Two examples -- 8.5 Some final thoughts -- 8.6 Summary -- Acknowledgments -- References -- Chapter 9: Nonparametric data analysis methods in medical imaging -- 9.1 Introduction -- 9.2 Shape analysis of the optic nerve head -- 9.3 Extraction of 3D data from CT scans -- 9.4 Means on manifolds -- 9.5 3D size-and-reflection shape manifold -- 9.6 3D size-and-reflection shape analysis of the human skull -- 9.7 DTI data analysis -- 9.8 MRI data analysis of corpus callosum image -- Acknowledgments -- References.

Chapter 10: Some families of distributions on higher shape spaces -- 10.1 Introduction -- 10.2 Shape distributions of angular central Gaussian type -- 10.3 Distributions without reflective symmetry -- 10.4 A test of reflective symmetry -- 10.5 Appendix: derivation of normalising constants -- References -- Chapter 11: Elastic registration and shape analysis of functional objects -- 11.1 Introduction -- 11.2 Registration in FDA: phase-amplitude separation -- 11.3 Elastic shape analysis of curves -- 11.4 Elastic shape analysis of surfaces -- 11.5 Metric-based image registration -- 11.6 Summary and future work -- References -- Part IV: Spatial, Image and Multivariate Analysis -- Chapter 12: Evaluation of diagnostics for hierarchical spatial statistical models -- 12.1 Introduction -- 12.2 Example: Sudden Infant Death Syndrome (SIDS) data for North Carolina -- 12.3 Diagnostics as instruments of discovery -- 12.4 Evaluation of diagnostics -- 12.5 Discussion and conclusions -- Acknowledgments -- References -- Chapter 13: Bayesian forecasting using spatiotemporal models with applications to ozone concentration levels in the Eastern United States -- 13.1 Introduction -- 13.2 Test data set -- 13.3 Forecasting methods -- 13.4 Forecast calibration methods -- 13.5 Results from a smaller data set -- 13.6 Analysis of the full Eastern US data set -- 13.7 Conclusion -- References -- Chapter 14: Visualisation -- 14.1 Introduction -- 14.2 The problem -- 14.3 A possible solution: self-explanatory visualisations -- References -- Chapter 15: Fingerprint image analysis: role of orientation patch and ridge structure dictionaries -- 15.1 Introduction -- 15.2 Dictionary construction -- 15.3 Orientation field estimation using orientation patch dictionary -- 15.4 Latent segmentation and enhancement using ridge structure dictionary -- 15.5 Conclusions and future work.

References -- Part V: Bioinformatics -- Chapter 16: Do protein structures evolve around 'anchor' residues? -- 16.1 Introduction -- 16.2 Exploratory data analysis -- 16.3 Are the anchor residues artefacts? -- 16.4 Effect of gap-closing method on structure shape -- 16.5 Alternative to multiple structure alignment -- 16.6 Discussion -- References -- Chapter 17: Individualised divergences -- 17.1 The past: genealogy of divergences and the man of Anekāntavāda -- 17.2 The present: divergences and profile shape -- 17.3 The future: challenging data -- References -- Chapter 18: Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem -- 18.1 Introduction -- 18.2 Overview of the article -- 18.3 Probabilistic formulation -- 18.4 Local and non-local structure -- 18.5 The local model -- 18.6 The non-local model -- 18.7 The formulation of the joint model -- 18.8 Kullback-Leibler optimality -- 18.9 Link with statistical potentials -- 18.10 Conclusions and outlook -- Acknowledgments -- References -- Chapter 19: MAD-Bayes matching and alignment for labelled and unlabelled configurations -- 19.1 Introduction -- 19.2 Modelling protein matching and alignment -- 19.3 Gap priors and related models -- 19.4 MAD-Bayes -- 19.5 MAD-Bayes for unlabelled matching and alignment -- 19.6 Omniparametric optimisation of the objective function -- 19.7 MAD-Bayes in the sequence-labelled case -- 19.8 Other kinds of labelling -- 19.9 Simultaneous alignment of multiple configurations -- 19.10 Beyond MAD-Bayes to posterior approximation? -- 19.11 Practical uses of MAD-Bayes approximations -- Acknowledgments -- References -- Index -- Wiley Series in Probability and Statistics -- End User License Agreement.

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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.