State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.See also Shumway and Stoffer (2000) for an introduction. For most of the commonly ... This implies that starting the optimization routine from different starting points may lead to different maxima. It is therefore a good ... maxima. A rather flat likelihood is another problem that one may face when looking for a MLE. In this caseanbsp;...
|Title||:||Dynamic Linear Models with R|
|Author||:||Giovanni Petris, Sonia Petrone, Patrizia Campagnoli|
|Publisher||:||Springer Science & Business Media - 2009-06-12|