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Autor(en): 
  • Konstantinos Fokianos
  • Benjamin Kedem
  • Regression Models for Time Series Analysis 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  September 2002  
    Genre:  Schulbücher 
     
    Regression Analysis / Regressionsanalyse / Spezialthemen Statistik / Statistics / Statistics Special Topics / Statistik / Time Series / Zeitreihen / Zeitreihenanalyse
    ISBN:  9780471363552 
    EAN-Code: 
    9780471363552 
    Verlag:  Wiley 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  Wiley Series in Probability and Statistics  
    Dimensionen:  H 244 mm / B 159 mm / D 22 mm 
    Gewicht:  624 gr 
    Seiten:  360 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    A thorough review of the most current regression methods in time series analysis 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. 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. 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. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm * Prediction and interpolation * Stationary processes

      



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