Design and cost optimization for hierarchical data

Kari Tokola, Andreas Lundell, Jaakko Nevalainen, Hannu Oja

    Research output: Contribution to journalArticleScientificpeer-review

    1 Citation (Scopus)


    In this paper, we consider balanced hierarchical data designs for both one-sample and two-sample (two-treatment) location problems. The variances of the relevant estimates and the powers of the tests strongly depend on the data structure through the variance components at each hierarchical level. Also, the costs of a design may depend on the number of units at different hierarchy levels, and these costs may be different for the two treatments. Finally, the number of units at different levels may be restricted by several constraints. Knowledge of the variance components, the costs at each level, and the constraints allow us to find the optimal design. Solving such problems often requires advanced optimization tools and techniques, which we briefly explain in the paper. We develop new analytical tools for sample size calculations and cost optimization and apply our method to a data set on Baltic herring.
    Original languageUndefined/Unknown
    Pages (from-to)130–148
    Number of pages19
    JournalStatistica Neerlandica
    Issue number2
    Publication statusPublished - 2014
    MoE publication typeA1 Journal article-refereed


    • clustered data
    • data design
    • multilevel data
    • sample size calculation

    Cite this