RESEARCH ON PATTERNED COVARIANCES

Project Details

Description

The proposed research is intended to fill major gaps in the statistical
analysis of biomedical data that involve models with patterned means and
patterned covariances possibly with missing data. These models arise
naturally in the analysis of longitudinal data and some genetics models.
Further work on statistical techniques and computer software developed by
the Principal Investigator to implement these techniques will be pursued
during this grant period with a long range goal of developing highly
disseminated, easy to use software (e.g., for SAS or BMDP). Research efforts in this area will include: (1) the completion of
documentation and testing of developed computer software; (2) testing of
performance characteristics both of the computer software and the
underlying statistical procedures; (3) formulating programmable procedures
for finding approximate null distributions for explicit MLE patterns; (4)
finding asymptotic nonnull distributions under local alternative
hypotheses; (5) characterizing the class of linear covariance paterns that
are submatrices of linear covariance paterns with explicit MLE; and (6)
publishing applications of these new statistical techniques in journals
accessible to applied researchers. This proposal addresses the difficulties of developing and implementing
statistical techniques which are relatively easy to interpret once the
results are in hand even though deriving the results requires sophisticated
mathematics. The essence of making statistical techniques available to
biomedical researchers is to have software which implements the
techniques. When software is not available, researchers resort to
alternative (and at times inefficient or incorrect) techniques for which
computer software is available.
StatusFinished
Effective start/end date12/31/896/30/90

Funding

  • National Institute of General Medical Sciences

ASJC

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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