Regression Models for Time Series Analysis

Name: Regression Models for Time Series Analysis
Price: 14.8 USD
Availability: InStock
ISBN: 9780471363552

ISBN-10: 0471363553

ISBN-13: 9780471363552

Edition: 2002

Authors: Benjamin Kedem, Konstantinos Fokianos

List price: $177.00

This item qualifies for FREE shipping.

30 day, 100% satisfaction guarantee!

Buy new: $167.19

Marketplace

3 new & used from $14.80

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

This volume captures the spirit of relatively recent developments in statistical practice and theory, gives many useful real data examples, and is accessible to practitioners and beginning graduate students of time series analysis and longitudinal data.

Book details

List price: $177.00
Copyright year: 2002
Publisher: John Wiley & Sons, Incorporated
Publication date: 8/19/2002
Binding: Hardcover
Pages: 360
Size: 6.00" wide x 9.25" long x 1.00" tall
Weight: 1.386
Language: English



Dedication


Preface



Time Series Following Generalized Linear Models



Partial Likelihood



Generalized Linear Models and Time Series



Partial Likelihood Inference



Estimation of the Dispersion Parameter



Iterative Reweighted Least Squares



Asymptotic Theory



Uniqueness and Existence



Large Sample Properties



Testing Hypotheses



Diagnostics



Deviance



Model Selection Criteria



Residuals



Quasi-Partial Likelihood



Generalized Estimating Equations



Real Data Examples



A Note on Computation



A Note on Model Building



Analysis of Mortality Count Data



Application to Evapotranspiration



Problems and Complements



Regression Models for Binary Time Series



Link Functions for Binary Time Series



The Logistic Regression Model



Probit and Other Links



Partial Likelihood Estimation



Inference for Logistic Regression



Asymptotic Relative Efficiency



Goodness of Fit



Deviance



Goodness of Fit Based on Response Classification



Real Data Examples



Rainfall Prediction



Modeling Successive Eruptions



Stock Price Prediction



Modeling Sleep Data



Problems and Complements



Regression Models for Categorical Time Series



Modeling



Link Functions for Categorical Time Series



Models for Nominal Time Series



Models for Ordinal Time Series



Partial Likelihood Estimation



Inference for m=3



Inference for m>3



Large Sample Theory



Inference for the Multinomial Logit Model



Testing Hypotheses



Goodness of Fit



Goodness of Fit Based on Response Classification



Power Divergence Family of Goodness of Fit Tests



A Family of Goodness of Fit Tests



Further Diagnostic Tools



Examples



Explanatory Analysis of DNA Sequence Data



Soccer Forecasting



Sleep Data Revisited



Additional Topics



Alternative Modeling



Spectral Analysis



Longitudinal Data



Problems and Complements



Asymptotic Theory



Regression Models for Count Time Series



Modeling



Models for Time Series of Counts



The Poisson Model



The Doubly Truncated Poisson Model



The Zeger--Qaqish Model



Inference



Partial Likelihood Estimation for the Poisson Model



Asymptotic Theory



Prediction Intervals



Inference for the Zeger--Qaqish Model



Hypothesis Testing



Goodness of Fit



Deviance



Residuals



Data Examples



Monthly Count of Rainy Days



Tourist Arrival Data



Problems and Complements



Other Models and Alternative Approaches



Integer Autoregressive and Moving Average Models



Branching Processes with Immigration



Integer Autoregressive Models of Order 1



Estimation for INAR(1) Process



Integer Autoregressive Models of Order p



Regression Analysis of Integer Autoregressive Models



Integer Moving Average Models



Extensions and Modifications



Discrete Autoregressive Moving Average Models



The Mixture Transition Distribution Model



Estimation in MTD Models



Old Faithful Data Revisited



Explanatory Analysis of DNA Sequence Data Revisited



Soccer Forecasting Data Revisited



Hidden Markov Models



Variable Mixture Models



Threshold Models



Partial Likelihood Inference



Comparison with the Threshold Model



ARCH Models



The ARCH(1) Model



Maximum Likelihood Estimation



Extensions of ARCH Models



Sinusoidal Regression Model



Mixed Models for Longitudinal Data



Problems and Complements



State Space Models



Introduction



Historical Note



Linear Gaussian State Space Models



Examples of Linear State Space Models



Estimation by Kalman Filtering and Smoothing



Estimation in the Linear Gaussian Model



Nonlinear and Non-Gaussian State Space Models



General Filtering and Smoothing



Dynamic Generalized Linear Models



Simulation Based Methods for State Space Models



A Brief MCMC Tutorial



MCMC Inference for State Space Models



Sequential Monte Carlo Sampling Methods



Likelihood Inference



Longitudinal Data



Kalman Filtering in Space-Time Data



Problems and Complements



Prediction and Interpolation



Introduction



Elements of Stationary Random Fields



Ordinary Kriging



Bayesian Spatial Prediction



An Auxiliary Gaussian Process



The Likelihood



Prior and Posterior of Model Parameters



Prediction of Z[subscript 0]



Numerical Algorithm for the Case k = 1



Normalizing Transformations



Software for BTG Implementation



Applications of BTG



Spatial Rainfall Prediction



Comparison with Kriging



Time Series Prediction



Seasonal Time Series



Problems and Complements



Elements of Stationary Processes


References


Index