A Composite Binomial Model Derived from Correlated Random Variables

Details

• Personal Author:
  Groff, Jensen H. ; Schlecht, Paul C. ; Song R
• Description:
  Motivated by a correlation problem in a power calculation of proficiency testing, a composite binomial model is developed to describe the distribution for the number of outliers from measurement results of multiple analytes contained in a single sampler. This model is different from other binomial models in that its component Bernoulli variables are derived from correlated random variables. By modeling the correlated random variables and selecting a binary link function to convert these correlated random variables to Bernoulli variables, various composite binomial distributions can be derived. Formulas for calculating the probabilities of the composite binomial distribution are provided. Although .the composite binomial model is developed for a power calculation, it can be applied to other problems related to a sum of correlated Bernoulli variables. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Analytical Methods Analytical Models Author Keywords: Outlier Bernoulli Variable Exchangeable Link Function Mathematical Models Models Samplers Sampling Methods
• ISSN:
  0361-0926
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  DPSE - Division of Physical Sciences and Engineering
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  29
• Issue:
  5
• NIOSHTIC Number:
  nn:20020815
• Citation:
  Commun Stat Theory Methods 2000 Jan; 29(5-6):1179-1195
• Contact Point Address:
  HGO/NIOSH, 4676 Columbia Parkway, Cincinnati, OH 45226, USA
• Federal Fiscal Year:
  2000
• Peer Reviewed:
  True
• Source Full Name:
  Communications in Statistics - Theory and Methods
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:bf4e0faf36509348d80530c617caa7d05f1441ddef423959b95fd95bb67421a69d7deab56cff3c2fde5ad10174be42e2af6a6ee2bf9e399e331e39f5bf6b9ae0
• Download URL:
  https://stacks.cdc.gov/view/cdc/198480/cdc_198480_DS1.pdf
• File Type:
  [PDF - 625.52 KB ]