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Dale L. Zimmerman, Vicente A. Nunez-Anton, "Antedependence Models for Longitudinal Data (Monographs on Statistics and Applied Probability)"

Although antedependence models are particularly useful for modeling longitudinal data that exhibit serial correlation, few books adequately cover these models. By gathering results scattered throughout the literature, Antedependence Models for Longitudinal Data offers a convenient, systematic way to learn about antedependence models. Illustrated with numerous examples, the book also covers some important statistical inference procedures associated with these models.

After describing unstructured and structured antedependence models and their properties, the authors discuss informal model identification via simple summary statistics and graphical methods. They then present formal likelihood-based procedures for normal antedependence models, including maximum likelihood and residual maximum likelihood estimation of parameters as well as likelihood ratio tests and penalized likelihood model selection criteria for the model’s covariance structure and mean structure. The authors also compare the performance of antedependence models to other models commonly used for longitudinal data.

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	Hidden Markov Models for Time Series: An Introduction Using R (Monographs on Statistics and Applied Probability) Hidden Markov Models for Time SeriesWalter Zucchini, Iain L. MacDonald \| Chapman & Hall/CRC \| 2009-04-28 \| ISBN:1584885734 \| Pages: 269 \| PDF \| 5.3MBThis book illustrates the wonderful flexibility of HMMs as general-purpose models for t ...
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