Buch, Englisch, Band 36, 544 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 914 g
Buch, Englisch, Band 36, 544 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 914 g
Reihe: Oxford Statistical Science Series
ISBN: 978-0-19-953302-2
Verlag: OXFORD UNIV PR
combination with smoothness priors for the basis coefficients.
Beginning with a review of basic methods for smoothing and mixed models, longitudinal data, spatial data and event history data are treated in separate chapters. Worked examples from various fields such as forestry, development economics, medicine and marketing are used to illustrate the statistical methods covered in this book. Most of these examples have been analysed using implementations in the Bayesian software, BayesX, and some with R Codes. These, as well as some of the data sets, are
made publicly available on the website accompanying this book.
Zielgruppe
Suitable for graduates, PhD students and their lecturers as a basis, or as additional material, for courses in statistics, biostatistics and econometrics. Also suitable for researchers in applied statistics, quantitative economics, the social sciences and the life sciences.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
Weitere Infos & Material
1: Introduction: Scope of the Book and Applications
2: Basic Concepts for Smoothing and Semiparametric Regression
3: Generalised Linear Mixed Models
4: Semiparametric Mixed Models for Longitudinal Data
5: Spatial Smothing, Interactions and Geoadditive Regression
6: Event History Data