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Short description Inference and Prediction in Large Dimensions offers a predominantly theoretical coverage of statistical prediction, with some potential applications discussed, when data and/or parameters belong to a large or infinite dimensional space. Authors Denis Bosq and Delphine Balnke develop the theory of statistical prediction, non-parametric estimation by adaptive projection, with applications to tests of fit and prediction, and theory of linear processes in function spaces with applications to prediction of continuous time processes.
From the contents List of abbreviations.
Part I: Statistical Prediction Theory.
1. Statistical Prediction.
2. Asymptotic Prediction.
Part II: Inference by Projection.
3. Estimation by adaptive projection.
4. Functional tests of fit.
5. Prediction by projection.
Part III: Inference by Kernels.
6. Kernel method in discrete time.
7. Kernel method in continuous time.
8. Kernel method from sampled data.
Part IV: Local Time.
9. The empirical density.
Part V: Linear Processes in High Dimensions.
10. Functional linear processes.
11. Estimation and prediction of functional linear processes.