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Bootstrap technique is a useful tool for assessing uncertainty in statistical estimation and thus it is widely applied for risk management. Bootstrap is without doubt a promising technique, however, it is not applicable to all time series models. A wrong application could lead to a false decision to take too much risk.§§Kenichi Shimizu investigates the limit of the two standard bootstrap techniques, the residual and the wild bootstrap, when these are applied to the conditionally heteroscedastic models, such as the ARCH and GARCH models. The author shows that the wild bootstrap usually does...

In modern scanning electron microscopy, sample surface preparation is of key importance, just as it is in transmission electron microscopy. With the procedures for sample surface preparation provided in the present book, the enormous potential of advanced scanning electron microscopes can be realized fully. This will take the reader to an entirely new level of scanning electron microscopy and finely-detailed images never seen before.

In modern scanning electron microscopy, sample surface preparation is of key importance. This book presents the procedures for sample surface preparation, thereby facilitating unprecedented capabilities with advanced scanning electron microscopes.

Bayesian Approaches to Shrinkage and Sparse Estimation introduces the reader to the world of Bayesian model determination by surveying modern shrinkage and variable selection algorithms and methodologies. Bayesian inference is a natural probabilistic framework for quantifying uncertainty and learning about model parameters, and this feature is particularly important for inference in modern models of high dimensions and increased complexity. The authors begin with a linear regression setting in order to introduce various classes of priors that lead to shrinkage/sparse estimators of...