Literaturnachweis - Detailanzeige
| Autor/in | McElreath, Richard |
|---|---|
| Titel | Statistical rethinking. A Bayesian course with examples in R and Stan. Second edition. |
| Quelle | Boca Raton; London; New York: CRC Press, Taylor & Francis Group (2020), xvii, 593 S. |
| Reihe | Texts in statistical science series; A Chapman & Hall book |
| Beigaben | Illustrationen; Diagramme; Literaturangaben S. 573-583 |
| Zusatzinformation | Inhaltsverzeichnis Titelbild |
| Sprache | englisch |
| Dokumenttyp | gedruckt; Monographie |
| ISBN | 0-367-13991-X; 978-0-367-13991-9 |
| Schlagwörter | Mehrebenenanalyse; Methodologie; Datenanalyse; Programmiersprache; Regressionsanalyse; Wahrscheinlichkeitsrechnung; Statistik; Daten; Forschungsdaten; Modell; Statistische Methode |
| Abstract | [This book] builds the knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes the readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that they understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding. The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with a chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses. (DIPF/Orig.). |
| Erfasst von | DIPF | Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main |
| Update | 2023/1 |
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