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Small area estimation under a multivariate linear model for repeated measures data

Author

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  • Innocent Ngaruye
  • Joseph Nzabanita
  • Dietrich von Rosen
  • Martin Singull

Abstract

In this article, small area estimation under a multivariate linear model for repeated measures data is considered. The proposed model aims to get a model which borrows strength both across small areas and over time. The model accounts for repeated surveys, grouped response units, and random effects variations. Estimation of model parameters is discussed within a likelihood based approach. Prediction of random effects, small area means across time points, and per group units are derived. A parametric bootstrap method is proposed for estimating the mean squared error of the predicted small area means. Results are supported by a simulation study.

Suggested Citation

  • Innocent Ngaruye & Joseph Nzabanita & Dietrich von Rosen & Martin Singull, 2017. "Small area estimation under a multivariate linear model for repeated measures data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(21), pages 10835-10850, November.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:21:p:10835-10850
    DOI: 10.1080/03610926.2016.1248784
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    Cited by:

    1. Hao Sun & Emily Berg & Zhengyuan Zhu, 2022. "Bivariate small‐area estimation for binary and gaussian variables based on a conditionally specified model," Biometrics, The International Biometric Society, vol. 78(4), pages 1555-1565, December.
    2. Angelo Moretti & Natalie Shlomo & Joseph W. Sakshaug, 2020. "Multivariate Small Area Estimation of Multidimensional Latent Economic Well‐being Indicators," International Statistical Review, International Statistical Institute, vol. 88(1), pages 1-28, April.
    3. María Dolores Esteban & María José Lombardía & Esther López‐Vizcaíno & Domingo Morales & Agustín Pérez, 2022. "Empirical best prediction of small area bivariate parameters," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1699-1727, December.

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