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This book tries to do three things. The first goal is to give an exposition of certain modes of stochastic convergence, in particular convergence in distribution. The classical …
This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about …
This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about …
During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and …
During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and …
This book provides a unified introduction to a variety of computational algorithms for Bayesian and likelihood inference. In this third edition, I have attempted to expand the …
This book provides a unified introduction to a variety of computational algorithms for Bayesian and likelihood inference. In this third edition, I have attempted to expand the …
This edition contains a large number of additions and corrections scattered throughout the text, including the incorporation of a new chapter on state-space models. The companion …
A text for graduate students in statistics, mathematics, engineering, and natural and social sciences. Covers such subjects as multivariate series, long-range dependence, infinite …
In general terms, the shape of an object, data set, or image can be de fined as the total of all information that is invariant under translations, rotations, and isotropic …