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Regression Models for Time Series Analysis (Wiley Series in Probability and Statistics) by Benjamin Kedem, Konstantinos Fokianos is a nonfiction available to read on EtoBox.

What is Regression Models for Time Series Analysis (Wiley Series in Probability and Statistics) about?

<p>A thorough review of the most current regression methods in time series analysis<br> 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.<br> 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.<br> 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,

Who reads Regression Models for Time Series Analysis (Wiley Series in Probability and Statistics)?

Author

Benjamin Kedem, Konstantinos Fokianos

Publisher

John Wiley & Sons, Incorporated

Published

2002

Language

EN

ISBN

9780471266983

Category

nonfiction

Subjects

Mathematics, Economics, Stem