{"product_id":"time-series-modeling-computation-and-inference-second-edition-paperback","title":"Time Series: Modeling, Computation, and Inference, Second Edition - Paperback","description":"\u003cp\u003eby \u003cb\u003eRaquel Prado\u003c\/b\u003e (Author), \u003cb\u003eMarco A. R. Ferreira\u003c\/b\u003e (Author), \u003cb\u003eMike West\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eFocusing on Bayesian approaches and computations using analytic and simulation-based methods for inference, \u003cb\u003e\u003ci\u003eTime Series: Modeling, Computation, and Inference, Second Edition\u003c\/i\u003e\u003c\/b\u003e integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling, analysis and forecasting, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and contacts research frontiers in multivariate time series modeling and forecasting. \u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003eIt presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. It explores the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian formulations and computation, including use of computations based on Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. It illustrates the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, environmental science, and finance. \u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003eAlong with core models and methods, the book represents state-of-the art approaches to analysis and forecasting in challenging time series problems. It also demonstrates the growth of time series analysis into new application areas in recent years, and contacts recent and relevant modeling developments and research challenges. \u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cb\u003e \u003c\/b\u003e\u003cp\u003eNew in the second edition: \u003c\/p\u003e \u003cul\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eExpanded on aspects of core model theory and methodology.\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eMultiple new examples and exercises.\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eDetailed development of dynamic factor models.\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eUpdated discussion and connections with recent and current research frontiers.\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eRaquel Prado\u003c\/strong\u003e is Professor in the Department of Statistics at the Baskin School of Engineering of the University of California Santa Cruz, USA. Her main research areas are time series analysis and Bayesian modeling - with a focus on analysis of large-dimensional nonstationary time series data and applications to biomedical signal processing and brain imaging. \u003cstrong\u003eMarco A. R. Ferreira\u003c\/strong\u003e is an Associate Professor in the Department of Statistics at Virginia Tech, where he served from 2016 to 2020 as the Director of Graduate Programs. \u003cstrong\u003eMike West\u003c\/strong\u003e holds a Duke University distinguished chair as the Arts \u0026amp; Sciences Professor of Statistics \u0026amp; Decision Sciences in the Department of Statistical Science, where he led the development of statistics from 1990-2002. \u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 452\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.95 x 9.21 x 6.14 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 25, 2023\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42702585593919,"sku":"9781032040042","price":128.28,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/4c589b28c2e8edf81cc7fb01700d6817_cc5802a3-a983-4525-b033-d77d8070774c.webp?v=1765025364","url":"https:\/\/dhlswag.com\/products\/time-series-modeling-computation-and-inference-second-edition-paperback","provider":"BBB","version":"1.0","type":"link"}