Random effects linear models for process mean and variance

So Young Sohn, C. J. Park

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


We consider random effects models for both the mean and variance of a process where some variations in both the mean and variance can be explained by their related covariate effects. Empirical Bayes procedures are employed to estimate covariate effects. Approaches proposed are applied to evaluate the performance of the Phase V sensors of the tactical remote sensor system of the U.S. Marine Corps in terms of their mean and variance of detection distance with respect to target types and sensitivity levels of a sensor.

Original languageEnglish
Pages (from-to)33-39
Number of pages7
JournalJournal of Quality Technology
Issue number1
Publication statusPublished - 1998 Jan

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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