Regression Models for Time Series Analysis

Szerző: Benjamin Kedem, Konstantinos Fokianos

Nyelv:

Kötés: Kemény kötésű

Kiadó: John Wiley & Sons Inc

Elérhetőség: Beszállítói készleten

Küldés 10-18 napon belül

70 243 Ft

A thorough review of the most current regression methods in time series analysis Regression methods...

Információk a könyvről

Szerző

Benjamin Kedem, Konstantinos Fokianos

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2002

oldal

360

EAN

9780471363552

ISBN

0471363553

Enbook ID

04890143

Kiadó

John Wiley & Sons Inc

Súly

612

Méretek

166 x 236 x 26

Teljes leírás

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

Érdekelheti

Azok a vásárlók, akik ezt a könyvet megvásárolták, a következőket is megvásárolták

Leggyakrabban keresett

Categories

Authors

Publishers

Termékek

Termékek

Leggyakrabban keresett

Categories

Authors

Publishers

Termékek

Termékek

Regression Models for Time Series Analysis

Információk a könyvről

Teljes leírás

Érdekelheti

Azok a vásárlók, akik ezt a könyvet megvásárolták, a következőket is megvásárolták