Time Series Analysis by State Space Methods (Oxford Statistical Science Series)
Views: 5
Review
... provides an up-to-date exposition and comprehensive treatment of state space models in time series analysis. (Journal of the Royal Statistical Society )This book will be helpful to graduate students and applied statisticians working in the area of econometric modelling as well as researchers in the areas of engineering, medicine and biology where state space models are used. (Journal of the Royal Statistical Society )
... a good mixture of theory and practical applications ... graduate and research students will definitely enjoy this book. Also practitioners will find the book quite useful. I would also recommend it for library purchase. (Journal of the Royal Statistical Society )
Product Description
This excellent text provides a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbence terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. The book provides an excellent source for the development of practical courses on time series analysis.Related Posts
- Collins GCSE Statistics - AQA GCSE Statistics Teacher's Pack
- The Contemporary British Society Reader
- Applied Multiple Regression: Correlation Analysis for the Behavioural Sciences
- Euclid's Window : The Story of Geometry from Parallel Lines to Hyperspace
- The CIA World Factbook 2012
- Forecasting, Structural Time Series Models and the Kalman Filter
- The Analysis of Time Series: An Introduction
- Statistics for Linguistics with R: A Practical Introduction (Mouton Textbook)
- Mechanics 1 for OCR (Cambridge Advanced Level Mathematics)
- The Cartoon Guide to Genetics
Tags: r programming language, statistics, computational statistics, r language, r programming