Bivariate Integer-Valued Time Series Models

Auteur

Sunecher Yuvraj is currently appointed as Senior Lecturer of Statistics and Finance at the School of Business, Management and Finance at the University of Technology Mauritius since 2011. He received his Doctorate in Statistics from the University of Mauritius in 2018.

Mamode Khan Naushad is an Associate Professor in Statistics at the Faculty of Social Sciences and Humanities at the University of Mauritius. He was awarded his PhD in Statistics from the University of Mauritius in 2010.

Vandna Jowaheer is a Professor and Chair in Applied Statistics at the University of Mauritius, Mauritius. She is an elected member of International Statistical Institute and a fellow of the African academic network.

Texte du rabat

This book proposes some novel models based on the autoregressive and moving average structures under various distributional assumptions of the innovation series for analysing non-stationary bivariate time series of counts. A useful resource for scholars, researchers and academics in the field of time series models.

Contenu

Table of Contents

Acknowledgements

Preface

About the Authors

1. Introduction

2. Constrained Binar(1) Model with Correlated Poisson Innovations

3. Constrained Binma(1) Model with Corelated Poisson Innovations

4. Unconstrained Binar(1) Model with Poisson Innovations

5. Unconstrained Binma(1) Model with Poisson Innovations

6. Constrained Binar(1) Model with Correlated NB Innovations

7. Constrained Binma(1) Model with Correlated NB Innovations

8. Unconstrained Binar(1) Model with NB Innovations

9. Unconstrained Binma(1) Model with NB Innovations

10. Constrained Binar(1) Model with Correlated Com-Poisson Innovations

11. Constrained Binma(1) Model with correlated Com-Poisson Innovations

12. Unconstrained Binar(1) Model with Com-Poisson Innovations

13. Unconstrained Binma(1) Model with Com-Poisson Innovations

14. Conclusion and Future Directions

Bibliography