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Mathematical statistics --- Linear models (Statistics) --- Data processing --- GLIM --- GLIM (Computer programs) --- Data processing. --- Linear models (Statistics) - Data processing

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Linear models (Statistics) --- -Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics --- Data processing --- GLIM. --- Data processing. --- Programming --- Models, Linear (Statistics) --- Monograph --- Linear models (Statistics) - Data processing.

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519.862 --- 519.862 Descriptive models. Econometrics --- Descriptive models. Econometrics --- Linear models (Statistics) --- 519.237 --- 519.237 Multivariate statistical methods --- Multivariate statistical methods --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics --- Data processing --- GLIM. --- GLIM (Computer programs) --- Linear models (Statistics) - Data processing

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This book provides a clear explanation of mixed modeling techniques, introducing their application through the analyses of real datasets and presenting each example using the most commonly used software packages - R, SAS, SPSS, HLM, and STATA. The authors describe common research designs and corresponding data structures for which mixed models analysis is an appropriate statistical tool, and they include detailed descriptions on how to set up datasets for these types of analyses. The treatment also includes real-world datasets that contain common problems, such as missing data, that must be addressed using mixed models. A supporting Web site provides software code and the datasets discussed in the book.

Linear models (Statistics) --- Data processing. --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 519.2 --- 681.3*G3 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics --- Data processing --- Methoden en technieken --- statistiek. --- Linear models (Statistics) - Data processing --- Statistiek.

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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. Giovanni Petris is Associate Professor at the University of Arkansas. He has published many articles on time series analysis, Bayesian methods, and Monte Carlo techniques, and has served on National Science Foundation review panels. He regularly teaches courses on time series analysis at various universities in the US and in Italy. An active participant on the R mailing lists, he has developed and maintains a couple of contributed packages. Sonia Petrone is Associate Professor of Statistics at Bocconi University,Milano. She has published research papers in top journals in the areas of Bayesian inference, Bayesian nonparametrics, and latent variables models. She is interested in Bayesian nonparametric methods for dynamic systems and state space models and is an active member of the International Society of Bayesian Analysis. Patrizia Campagnoli received her PhD in Mathematical Statistics from the University of Pavia in 2002. She was Assistant Professor at the University of Milano-Bicocca and currently works for a financial software company.

Linear models (Statistics) -- Data processing. --- R (Computer program language). --- State-space methods. --- R (Computer program language) --- Linear models (Statistics) --- State-space methods --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Data processing --- Bayesian statistical decision theory. --- Monte Carlo method. --- Artificial sampling --- Model sampling --- Monte Carlo simulation --- Monte Carlo simulation method --- Stochastic sampling --- Bayes' solution --- Bayesian analysis --- Mathematical statistics. --- Statistical Theory and Methods. --- Games of chance (Mathematics) --- Mathematical models --- Numerical analysis --- Numerical calculations --- Stochastic processes --- Statistical decision --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Statistics. --- GNU-S (Computer program language) --- Domain-specific programming languages