{"product_id":"regression-models-for-time-series-analysis-hardback-9780471363552","title":"Regression Models for Time Series Analysis (Hardback) 9780471363552","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eRegression Models for Time Series Analysis\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eBenjamin Kedem (Author), Konstantinos Fokianos (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471363552, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 2 September 2002\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e360 pages, Photos: 1 B\u0026amp;W, 0 Color; Screen captures: 1 B\u0026amp;W, 0 Color; Tables: 42 B\u0026amp;W, 0 Color; Graphs: 72 B\u0026amp;W, 0 Color\u003cbr\u003e24.4 x 16.2 x 2.3 cm, 0.643 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"...provides an excellent overview of modern regression methods in time series analysis...accessible and illustrative...a valuable resource to students, researchers, and practitioners.  The text reflects a deep appreciation of both theory and applications, as well as a comprehensive understanding of a set of modeling frameworks that are increasingly integral to modern time series analysis.\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, March 2004)  \u003cp\u003e\"...highly recommended...\" (\u003ci\u003eChoice\u003c\/i\u003e, Vol. 40, No. 6, February 2003)\u003c\/p\u003e \u003cp\u003e\"...the book does what it sets out to do very well and will be useful for both practitioners and researchers...\" (\u003ci\u003eShort Book Reviews\u003c\/i\u003e, April 2003)\u003c\/p\u003e \u003cp\u003e\"...can be recommended to teachers and students as material for seminars and special lectures...very useful for applied statisticians.\" (\u003ci\u003eZentralblatt Math\u003c\/i\u003e, Vol.1011, No.11, 2003)\u003c\/p\u003e \u003cp\u003e\"...introduces the reader to relatively newer and somewhat more diverse regression models and methods for time series analysis than most standard texts.\" (\u003ci\u003eQuarterly of Applied Mathematics\u003c\/i\u003e, Vol. LXI, No. 2, June 2003)\u003c\/p\u003e \u003cp\u003e\"...I gladly recommend this book...\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, Vol. 45, No. 4, November 2003)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eA thorough review of the most current regression methods in time series analysis\u003cbr\u003e Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis.\u003cbr\u003e Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data.\u003cbr\u003e The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and\/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements.\u003cbr\u003e Notably, the book covers:\u003cbr\u003e * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling\u003cbr\u003e * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm\u003cbr\u003e * Prediction and interpolation\u003cbr\u003e * Stationary processes\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eDedication.\u003cbr\u003e \u003cbr\u003e Preface.\u003cbr\u003e \u003cbr\u003e Times Series Following Generalized Linear Models.\u003cbr\u003e \u003cbr\u003e Regression Models for Binary Time Series.\u003cbr\u003e \u003cbr\u003e Regression Models for Categorical Time Series.\u003cbr\u003e \u003cbr\u003e Regression Models for Count Time Series.\u003cbr\u003e \u003cbr\u003e Other Models and Alternative Approaches.\u003cbr\u003e \u003cbr\u003e State Space Models.\u003cbr\u003e \u003cbr\u003e Prediction and Interpolation.\u003cbr\u003e \u003cbr\u003e Appendix: Elements of Stationary Processes.\u003cbr\u003e \u003cbr\u003e References.\u003cbr\u003e \u003cbr\u003e Index.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Interscience","offers":[{"title":"Brand New","offer_id":52293434376472,"sku":"9780471363552","price":116.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780471363552.jpg?v=1781639858","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/regression-models-for-time-series-analysis-hardback-9780471363552","provider":"Freshly Printed Books","version":"1.0","type":"link"}