Longitudinal Data Analysis, Volume 50 in the Handbook of Statistics series covers how data consists of a series of repeated observations of the same subjects over an extended time frame and is thus useful for measuring change. Such studies and the data arise in a variety of fields, such as health sciences, genomic studies, experimental physics, sociology, sports and student enrollment in universities. For example, in health studies, intra-subject correlation of responses must be accounted for, covariates vary with time, and bias can arise if patients drop out of the study.
1. 1 Introduction - p. 109 2. 2 Linear Gaussian SSMs - p. 110 3. 3 Hierarchical state space models - p. 114 4. 4 Estimation, inference and prediction - p. 116 5. 5 Applications - p. 122 6. 6 Extensions to bivariate HSSMs - p. 129 7. 7 Extensions to nonlinear non-Gaussian HSSMs - p. 137 8. 8 Bibliographical notes - p. 148 9. References - p. 150 10. Chapter 5: Latent state-trait analysis - p. 155 11. Chapter 6: Recent advances in longitudinal data analysis - p. 173 12. Abstract - p. 173 13. 1 Introduction - p. 173 14. 2 Covariance selection - p. 179 15. 3 Variable selection - p. 185 16. 4 Machine learning approaches - p. 202 17. 5 Discussion - p. 214 18. References - p. 216 19. Part III: Applications - p. 221 20. Chapter 7: Government as population: Demographic perspectives on the United States legislative, judicial and executive branches, 1789–2020 - p. 225 21. Chapter 8: Beginnings: Formation and growth of natural phenomena out of Fisher information - p. 267 22. Abstract - p. 267 23. 1 Nature, as evolving, communicating systems: Essential role of background Fisher information - p. 268 24. 2 What has past use of the MFI principle shown? - p. 278 25. 3 Information aspects of system coordinates and parameters - p. 281 26. 4 Nature of the output: Schrodinger's dilemma - p. 283 27. 5 Concept of system “complexity” - p. 285 28. 6 Principle of maximum Fisher information (MFI) - p. 286 29. 7 Definition of Fisher information (FI) I - p. 289 30. 8 Summary to this point - p. 294 31. 9 MFI view of the fundamental physical constants per se as statistical entities - p. 296 32. 10 Discussion - p. 296 33. 11 Information channels: Basics of MFI principle - p. 300 34. 12 Application: Early growth laws p(t) for viruses, cancer and the cosmos - p. 306 35. 13 Regarding the fundamental need for well-defined Planck constants - p. 307 36. 14 On early growth of cancer, or virus, as quantum-based - p. 309 37. 15 Growth laws of viruses: Of cancer - p. 309 38. 16 Experimental verification - p. 312 39. 17 A sketch of past work on cosmic origins relating to the preceding theory of fluctuations - p. 312 40. 18 Possible mechanisms for a perpetual input information level J - p. 313 41. 19 Comparison for early growth of viruses, cancer, the cosmos - p. 314 42. References - p. 329 43. Further reading - p. 334 44. Index - p. 334
Longitudinal Data Analysis, Volume 50 in the Handbook of Statistics series covers how data consists of a series of repeated observations of the same subjects over an extended time frame and is thus useful for measuring change. Such studies and the data arise in a variety of fields, such as health sciences, genomic studies, experimental physics, sociology, sports and student enrollment in universities. For example, in health studies, intra-subject correlation of responses must be accounted for, covariates vary with time, and bias can arise if patients drop out of the study.
1. 1 Introduction - p. 109 2. 2 Linear Gaussian SSMs - p. 110 3. 3 Hierarchical state space models - p. 114 4. 4 Estimation, inference and prediction - p. 116 5. 5 Applications - p. 122 6. 6 Extensions to bivariate HSSMs - p. 129 7. 7 Extensions to nonlinear non-Gaussian HSSMs - p. 137 8. 8 Bibliographical notes - p. 148 9. References - p. 150 10. Chapter 5: Latent state-trait analysis - p. 155 11. Chapter 6: Recent advances in longitudinal data analysis - p. 173 12. Abstract - p. 173 13. 1 Introduction - p. 173 14. 2 Covariance selection - p. 179 15. 3 Variable selection - p. 185 16. 4 Machine learning approaches - p. 202 17. 5 Discussion - p. 214 18. References - p. 216 19. Part III: Applications - p. 221 20. Chapter 7: Government as population: Demographic perspectives on the United States legislative, judicial and executive branches, 1789–2020 - p. 225 21. Chapter 8: Beginnings: Formation and growth of natural phenomena out of Fisher information - p. 267 22. Abstract - p. 267 23. 1 Nature, as evolving, communicating systems: Essential role of background Fisher information - p. 268 24. 2 What has past use of the MFI principle shown? - p. 278 25. 3 Information aspects of system coordinates and parameters - p. 281 26. 4 Nature of the output: Schrodinger's dilemma - p. 283 27. 5 Concept of system “complexity” - p. 285 28. 6 Principle of maximum Fisher information (MFI) - p. 286 29. 7 Definition of Fisher information (FI) I - p. 289 30. 8 Summary to this point - p. 294 31. 9 MFI view of the fundamental physical constants per se as statistical entities - p. 296 32. 10 Discussion - p. 296 33. 11 Information channels: Basics of MFI principle - p. 300 34. 12 Application: Early growth laws p(t) for viruses, cancer and the cosmos - p. 306 35. 13 Regarding the fundamental need for well-defined Planck constants - p. 307 36. 14 On early growth of cancer, or virus, as quantum-based - p. 309 37. 15 Growth laws of viruses: Of cancer - p. 309 38. 16 Experimental verification - p. 312 39. 17 A sketch of past work on cosmic origins relating to the preceding theory of fluctuations - p. 312 40. 18 Possible mechanisms for a perpetual input information level J - p. 313 41. 19 Comparison for early growth of viruses, cancer, the cosmos - p. 314 42. References - p. 329 43. Further reading - p. 334 44. Index - p. 334
