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Regression Models for Time Series Analysis

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ISBN-10: 0471363553

ISBN-13: 9780471363552

Edition: 2002

Authors: Benjamin Kedem, Konstantinos Fokianos

List price: $177.00
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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.
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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

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
Model Selection Criteria
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
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
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
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
Models for Time Series of Counts
The Poisson Model
The Doubly Truncated Poisson Model
The Zeger--Qaqish Model
Partial Likelihood Estimation for the Poisson Model
Asymptotic Theory
Prediction Intervals
Inference for the Zeger--Qaqish Model
Hypothesis Testing
Goodness of Fit
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
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
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