LEADER 05496nam 2200661Ia 450 001 9910146069003321 005 20170815114533.0 010 $a1-280-25283-9 010 $a9786610252831 010 $a0-470-30356-5 010 $a0-471-46168-7 010 $a0-471-26698-1 035 $a(CKB)1000000000019008 035 $a(EBL)226533 035 $a(OCoLC)475932701 035 $a(SSID)ssj0000234934 035 $a(PQKBManifestationID)11203275 035 $a(PQKBTitleCode)TC0000234934 035 $a(PQKBWorkID)10241898 035 $a(PQKB)11574365 035 $a(MiAaPQ)EBC226533 035 $a(EXLCZ)991000000000019008 100 $a20020718d2002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRegression models for time series analysis$b[electronic resource] /$fBenjamin Kedem, Konstantinos Fokianos 210 $aChichester ;$aHoboken, NJ $cJohn Wiley & Sons, Inc.$dc2002 215 $a1 online resource (361 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-36355-3 320 $aIncludes bibliographical references (p. 297-326) and index. 327 $aRegression Models for Time Series Analysis; Dedication; Contents; Preface; 1 Time Series Following Generalized Linear Models; 1.1 Partial Likelihood; 1.2 Generalized Linear Models and Time Series; 1.3 Partial Likelihood Inference; 1.3.1 Estimation of the Dispersion Parameter; 1.3.2 Iterative Reweighted Least Squares; 1.4 Asymptotic Theory; 1.4.1 Uniqueness and Existence; 1.4.2 Large Sample Properties; 1.5 Testing Hypotheses; 1.6 Diagnostics; 1.6.1 Deviance; 1.6.2 Model Selection Criteria; 1.6.3 Residuals; 1.7 Quasi-Partial Likelihood; 1.7.1 Generalized Estimating Equations 327 $a1.8 Real Data Examples1.8.1 A Note on Computation; 1.8.2 A Note on Model Building; 1.8.3 Analysis of Mortality Count Data; 1.8.4 Application to Evapotranspiration; 1.9 Problems and Complements; 2 Regression Models for Binary Time Series; 2.1 Link Functions for Binary Time Series; 2.1.1 The Logistic Regression Model; 2.1.2 Probit and Other Links; 2.2 Partial Likelihood Estimation; 2.3 Inference for Logistic Regression; 2.3.1 Asymptotic Relative Eficiency; 2.4 Goodness of Fit; 2.4.1 Deviance; 2.4.2 Goodness of Fit Based on Response Classification; 2.5 Real Data Examples 327 $a2.5.1 Rainfall Prediction2.5.2 Modeling Successive Eruptions; 2.5.3 Stock Price Prediction; 2.5.4 Modeling Sleep Data; 2.6 Problems and Complements; 3 Regression Models for Categorical Time Series; 3.1 Modeling; 3.2 Link Functions for Categorical Time Series; 3.2.1 Models for Nominal Time Series; 3.2.2 Models for Ordinal Time Series; 3.3 Partial Likelihood Estimation; 3.3.1 Inference for m=3; 3.3.2 Inference for m>3; 3.3.3 Large Sample Theory; 3.3.4 Inference for the Multinomial Logit Model; 3.3.5 Testing Hypotheses; 3.4 Goodness of Fit; 3.4.1 Goodness of Fit Based on Response Classification 327 $a3.4.2 Power Divergence Family of Goodness of Fit Tests3.4.3 A Family of Goodness of Fit Tests; 3.4.4 Further Diagnostic Tools; 3.5 Examples; 3.5.1 Explanatory Analysis of DNA Sequence Data; 3.5.2 Soccer Forecasting; 3.5.3 Sleep Data Revisited; 3.6 Additional Topics; 3.6.1 Alternative Modeling; 3.6.2 Spectral Analysis; 3.6.3 Longitudinal Data; 3.7 Problems and Complements; Appendix: Asymptotic Theory; 4 Regression Models for Count Time Series; 4.1 Modeling; 4.2 Models for Time Series of Counts; 4.2.1 The Poisson Model; 4.2.2 The Doubly Truncated Poisson Model; 4.2.3 The Zeger-Qaqish Model 327 $a4.3 Inference4.3.1 Partial Likelihood Estimation for the Poisson Model; 4.3.2 Asymptotic Theory; 4.3.3 Prediction Intervals; 4.3.4 Inference for the Zeger-Qaqish Model; 4.3.5 Hypothesis Testing; 4.4 Goodness of Fit; 4.4.1 Deviance; 4.4.2 Residuals; 4.5 Data Examples; 4.5.1 Monthly Count of Rainy Days; 4.5.2 Tourist Arrival Data; 4.6 Problems and Complements; 5 Other Models and Alternative Approaches; 5.1 Integer Autoregressive and Moving Average Models; 5.1.1 Branching Processes with Immigration; 5.1.2 Integer Autoregressive Models of Order 1; 5.1.3 Estimation for INAR( 1) Process 327 $a5.1.4 Integer Autoregressive Models of Order p 330 $aA thorough review of the most current regression methods in time series analysisRegression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis.Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examina 410 0$aWiley series in probability and statistics. 606 $aTime-series analysis 606 $aRegression analysis 608 $aElectronic books. 615 0$aTime-series analysis. 615 0$aRegression analysis. 676 $a519.55 700 $aKedem$b Benjamin$f1944-$0251532 701 $aFokianos$b Konstantinos$0614210 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910146069003321 996 $aRegression models for time series analysis$91129918 997 $aUNINA