ArticleCommunications in statistics: Simulation and computation2024
A weighted Jackknife approach utilizing linear model based-estimators for clustered data.
Article in Communications in statistics: Simulation and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Women's Preferences for Home-Based Self-Sampling or Clinic-Based Testing for Cervical Cancer Screening.JAMA network open · 2026Article
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5 authors.
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Abstract
Small number of clusters combined with cluster level heterogeneity poses a great challenge for the data analysis. We have published a weighted Jackknife approach to address this issue applying weighted cluster means as the basic estimators. The current study proposes a new version of the weighted delete-one-cluster Jackknife analytic framework, which employs Ordinary Least Squares or Generalized Least Squares estimators as the fundamentals. Algorithms for computing estimated variances of the study estimators have also been derived. Wald test statistics can be further obtained, and the statistical comparison in the outcome means of two conditions is determined using the cluster permutation procedure. The simulation studies show that the proposed framework produces estimates with higher precision and improved power for statistical hypothesis testing compared to other methods.
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