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Symmetric Regression Model for Temporal Data

Author

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  • Francisco JA Cysneiros

    (Departamento de Estatística, CCEN-UFPE - Cidade Universitária, Brazil)

Abstract

The pioneering work for non-Gaussian time series developed by Heyde & Feigin [1] who developed Poisson conditional autoregressive range. Cox [2] studied the autocorrelation of data, featuring two classes of time-dependent models: models conditioned on past observations and based on latent processes. In addition, Zeger [3] including the past and present of covariates in this model. Zeger & Qaqish [4] developed Markov chain for time series. Li [5] included moving averages models component in this model.

Suggested Citation

  • Francisco JA Cysneiros, 2018. "Symmetric Regression Model for Temporal Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(2), pages 44-45, February.
    • Handle: RePEc:adp:jbboaj:v:5:y:2018:i:2:p:44-45
    DOI: 10.19080/BBOAJ.2018.05.555657
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    References listed on IDEAS

    as
    1. Pena, Daniel, 1990. "Influential Observations in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 235-241, April.
    2. Andréa Rocha & Francisco Cribari-Neto, 2009. "Beta autoregressive moving average models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 529-545, November.
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