Beschreibung:
"This text provides a state-of-the-art treatment of distributional regression, accompanied by real-world examples from diverse areas of application. Maximum likelihood, Bayesian and machine learning approaches are covered in-depth and contrasted, providing an integrated perspective on GAMLSS for researchers in statistics and other data-rich fields"--
Preface; Notation and Termanology; Part I. Introduction and Basics: 1. Distributional Regression Models; 2. Distributions; 3. Additive Model Terms; Part II. Statistical Inference in GAMLSS: 4. Inferential Methods; 5. Penalized Maximum Likelihood Inference; 6. Bayesian Inference; 7. Statistical Boosting for GAMLSS; Part. III Applications and Case Studies: 8. Fetal Ultrasound; 9. Speech Intelligibility Testing; 10. Social Media Post Performance; 11. Childhood Undernutrition in India; 12. Socioeconomic Determinants of Federal Election Outcomes in Germany; 13. Variable Selection for Gene Expression Data; Appendix A. Continuous Distributions; Appendix B. Discrete Distributions; Bibliography; Index.