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Theoretical Advancements in Small Area Modeling: A Case Study with the CHILD Cohort

Author

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  • Charanpal Singh

    (Department of Community Health Sciences, University of Manitoba, Winnipeg, MB R3E 0W3, Canada
    Current address: S113 Medical Services Building, 750 Bannatyne Ave., Winnipeg, MB R3E 0W3, Canada.
    These authors contributed equally to this work.)

  • Mahmoud Torabi

    (Department of Community Health Sciences, University of Manitoba, Winnipeg, MB R3E 0W3, Canada
    These authors contributed equally to this work.)

Abstract

Developing accurate predictive models in statistical analysis presents significant challenges, especially in domains with limited routine assessments. This study aims to advance the theoretical underpinnings of longitudinal logistic and zero-inflated Poisson (ZIP) models in the context of small area estimation (SAE). Utilizing data from the Canadian Healthy Infant Longitudinal Development (CHILD) study as a case study, we explore the use of individual- and area-level random effects to enhance model precision and reliability. The study evaluates various covariates’ impact (such as mother’s asthma, mother wheezed, mother smoked) on model performance to predict child’s wheezing, emphasizing the role of location within Manitoba. Our main findings contribute to the literature by providing insights into the development and refinement of small area models, emphasizing the significance of advancing theoretical frameworks in statistical modeling.

Suggested Citation

  • Charanpal Singh & Mahmoud Torabi, 2025. "Theoretical Advancements in Small Area Modeling: A Case Study with the CHILD Cohort," Stats, MDPI, vol. 8(2), pages 1-23, May.
    • Handle: RePEc:gam:jstats:v:8:y:2025:i:2:p:39-:d:1657150
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    References listed on IDEAS

    as
    1. Jiming Jiang & Mahmoud Torabi, 2020. "Sumca: simple, unified, Monte‐Carlo‐assisted approach to second‐order unbiased mean‐squared prediction error estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(2), pages 467-485, April.
    2. Joshua C. C. Chan & Angelia L. Grant, 2016. "On the Observed-Data Deviance Information Criterion for Volatility Modeling," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 772-802.
    3. Torabi, Mahmoud & Shokoohi, Farhad, 2012. "Likelihood inference in small area estimation by combining time-series and cross-sectional data," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 213-221.
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