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Regression and Time Series Model Selection
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Techniques for univariate regression, autoregressive time series models, multivariate regression, vector autoregression, cross-validation, bootstrap, robust regression, nonparametric regression and wavelets are all covered. Many practical examples are given to illustrate the methods and there are also a number of useful simulation studies that appear in the book. The final chapter (Chapter 9) covers extensive simulations comparing many of the popular model selection criteria for both time series and regresion modeling.
My only disappointment is the omission of the recent developments in Bayesian model selection. At least the authors mention this omission upfront in Chapter 1 and provide good references to the literature.