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DetectionBiostat.pdf
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🔗 Source: eprints.ncrm.ac.uk
📊 Size: 1.12 MB
📄 Pages: 34 pages
⬇️ Downloads: 99

Summary

Bayesian model choice is proposed for detecting unusual temporal patterns in small area data, using a novel detection method that compares estimates resembling the overall temporal pattern to those retaining local temporal structures. A Monte Carlo procedure approximates null distributions, and classification rules are derived to control the false discovery rate. The method is applied to mortality data on chronic obstructive pulmonary disease in England and Wales, identifying an unusual district with higher hospital readmission and mortality rates. Simulation studies demonstrate the method's good performance in detecting various departure patterns while controlling the false discovery rate.

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Bayesian model choice is proposed for detecting unusual temporal patterns in small area data, using a novel detection method that compares estimates resembling...

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  • File Format: PDF
  • File Size: 1.12 MB
  • Pages: 34
  • Language: EN
  • Total Downloads: 99
  • Last Updated: 9 hours ago

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