{"product_id":"regression-models-for-time-series-analysis-9780471363552","title":"Regression Models for Time Series Analysis","description":"A 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\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Benjamin Kedem, Konstantinos Fokianos\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Wiley-Interscience\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 08\/19\/2002\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 360\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.39lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.70h x 6.10w x 0.88d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9780471363552\u003cbr\u003e\u003cbr\u003e\u003cb\u003eReview Citation(s): \u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChoice\u003c\/i\u003e 02\/01\/2003 pg. 1017\u003cbr\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003eBENJAMIN KEDEM, PhD, is Professor of Mathematics at the University of Maryland.\u003cbr\u003e KONSTANTINOS FOKIANOS, PhD, is Assistant Professor in the Department of Mathematics and Statistics at the University of Cyprus.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003eThis title is not returnable\u003c\/i\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"Wiley-Interscience","offers":[{"title":"Hardcover","offer_id":40110245642355,"sku":"9.78047E+12","price":200.5,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/products\/img_3d01e0db-537e-4fab-84b3-5ea3c287a01e.jpg?v=1653402846","url":"https:\/\/bookstorenmore.com\/products\/regression-models-for-time-series-analysis-9780471363552","provider":"Bookstore N More","version":"1.0","type":"link"}