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Circular Data Diagnostics in Regression and Time Series Models

In: Directional and Multivariate Statistics

Author

Listed:
  • Xiaoping Zhan

    (Sichuan University, School of Law)

  • Tiefeng Ma

    (School of Statistics, Southwestern University of Finance and Economics)

  • Shuangzhe Liu

    (University of Canberra, Faculty of Science and Technology)

  • Kunio Shimizu

    (Center for Training Professors in Statistics, The Institute of Statistical Mathematics)

Abstract

Circular data and their applications are pervasive across numerous disciplines. In the field of circular data analytics and statistical learning, distributional studies, regression and time series models have played crucial roles. In this paper, we present a comprehensive framework for circular regression and time series models. We employ the maximum likelihood estimation approach coupled with local and global influence methods to provide a robust methodology. Furthermore, we explore several specific models for analysing circular data and investigate techniques to identify influential observations within these models. To demonstrate the effectiveness of our proposed methods, we provide simulated and real data examples.

Suggested Citation

  • Xiaoping Zhan & Tiefeng Ma & Shuangzhe Liu & Kunio Shimizu, 2025. "Circular Data Diagnostics in Regression and Time Series Models," Springer Books, in: Somesh Kumar & Barry C. Arnold & Kunio Shimizu & Arnab Kumar Laha (ed.), Directional and Multivariate Statistics, pages 3-23, Springer.
    • Handle: RePEc:spr:sprchp:978-981-96-2004-3_1
    DOI: 10.1007/978-981-96-2004-3_1
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