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Time Series Regression for Counts: An Investigation into the Relationship between Sudden Infant Death Syndrome and Environmental Temperature

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  • M. J. Campbell

Abstract

Zeger's approach to the regression of count data which are serially ordered in time is discussed and compared with an empirical method. The methods are used to investigate the relationship between sudden infant death syndrome (SIDS) and environmental temperature in three data sets: for England and Wales 1979–83, England and Wales 1984–86 and New South Wales 1981–85. The results imply a strong negative relationship, in which the deaths due to SIDS increase 2–5 days after the environmental temperature goes down. The data are then explored to see whether there is a threshold in the temperature effect and to examine the consistency of the effect over time.

Suggested Citation

  • M. J. Campbell, 1994. "Time Series Regression for Counts: An Investigation into the Relationship between Sudden Infant Death Syndrome and Environmental Temperature," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(2), pages 191-208, March.
    • Handle: RePEc:bla:jorssa:v:157:y:1994:i:2:p:191-208
    DOI: 10.2307/2983358
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    Cited by:

    1. Tobías, Aureli & Saez, Marc, 2004. "Time-series regression models to study the short-term effects of environmental factors on health," Working Papers of the Department of Economics, University of Girona 11, Department of Economics, University of Girona.
    2. Neil Shephard, 1995. "Generalized linear autoregressions," Economics Papers 8., Economics Group, Nuffield College, University of Oxford.
    3. Wu, Rongning, 2012. "On variance estimation in a negative binomial time series regression model," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 145-155.
    4. Hope Corman & Dhaval Dave & Nancy E. Reichman, 2018. "Evolution of the Infant Health Production Function," Southern Economic Journal, John Wiley & Sons, vol. 85(1), pages 6-47, July.
    5. Shang, Zuofeng, 2012. "On latent process models in multi-dimensional space," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1259-1266.
    6. HEINEN, Andréas, 2003. "Modelling time series count data: an autoregressive conditional Poisson model," LIDAM Discussion Papers CORE 2003062, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Chigozie E. Utazi, 2017. "Bayesian Single Changepoint Estimation in a Parameter-driven Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 765-779, September.
    8. Richard A. Davis & Thiago do Rêgo Sousa & Claudia Klüppelberg, 2021. "Indirect inference for time series using the empirical characteristic function and control variates," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(5-6), pages 653-684, September.

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