Model Selection and Model Averaging (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 27)
Product Description
Model Selection and Model Averaging (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 27)
- Used Book in Good Condition
Choosing a model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. The uncertainties involved with model selection are tackled with discussions of frequent and Bayesian methods; model averaging schemes are presented. Real-data examples are complemented by derivations providing deeper insight into the methodology, and instructive exercises build familiarity with the methods. The companion website features Data sets and R-code.
Technical Specifications
You might also like
Deep Learning (Adaptive Computation and Machine Learning series)
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs)
Mathematical Foundations of Infinite-Dimensional Statistical Models (Cambridge Series in Statistical and Probabilistic Mathematics)
Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs)
Uncertainty: The Soul of Modeling, Probability & Statistics
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
Data Analysis Using Regression and Multilevel/Hierarchical Models
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction