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Comparison of designs for the three-fold nested random model

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  • Byoung Cheol Jung
  • André Khuri
  • Juneyoung Lee

Abstract

The quality of estimation of variance components depends on the design used as well as on the unknown values of the variance components. In this article, three designs are compared, namely, the balanced, staggered, and inverted nested designs for the three-fold nested random model. The comparison is based on the so-called quantile dispersion graphs using analysis of variance (ANOVA) and maximum likelihood (ML) estimates of the variance components. It is demonstrated that the staggered nested design gives more stable estimates of the variance component for the highest nesting factor than the balanced design. The reverse, however, is true in case of lower nested factors. A comparison between ANOVA and ML estimation of the variance components is also made using each of the aforementioned designs.

Suggested Citation

  • Byoung Cheol Jung & André Khuri & Juneyoung Lee, 2008. "Comparison of designs for the three-fold nested random model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(6), pages 701-715.
    • Handle: RePEc:taf:japsta:v:35:y:2008:i:6:p:701-715
    DOI: 10.1080/02664760801924079
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    Cited by:

    1. S. Mukhopadhyay & S. W. Looney, 2009. "Quantile dispersion graphs to compare the efficiencies of cluster randomized designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1293-1305.
    2. S.P. Singh & S. Mukhopadhyay & A. Roy, 2015. "Comparison of three-level cluster randomized trials using quantile dispersion graphs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1792-1812, August.

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