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Providing an accessible yet comprehensive introduction to this important subject, Time Series Analysis emphasizes basic methods for modeling linear dynamic systems. The text presents a solid theoretical understanding of basic concepts, such as multivariate random variables, stochastic processes, and regression-based methods, while also providing a number of practical, real-world examples. It covers advanced topics that include spectral analysis, state space models, and recursive estimation. With a variety of both written and computer-based exercises, the text gives computational...

Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data. Implementations using R are provided throughout the text, although other software packages are also discussed. Numerous examples show how the problems are solved with R.

After describing the necessary likelihood theory, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the corresponding/parallel...