Medical data often require complex models. For example, clinical
monitoring produces time induced correlation, and relationships among
variables change due to medical intervention. Many popularly used
biostatistical procedures depend on approximations made for mathematical
Resampling methods are computationally intensive techniques which
approximate the distribution of a statistic using only the observed data.
The two-fold advantages of resampling methods are that (1) they are
conceptually simple and (2) they often apply in complex problems
inaccessible through other techniques.
Phase I research will focus on designing software in the S-PLUS
environment in a way that facilitates use of resampling methods. We plan
to achieve this through judicious use of presentation tools, and through
a flexible software design. This design will support the different needs
of (1) data analysts and (2) biostatistical researchers who want to modify
and extend resampling capabilities. In Phase I we will prototype the
design, focusing on the linear model.
Resampling methods offer the opportunity to revolutionize statistical
practice: making it easier to use and understand, yet applicable to
PROPOSED COMMERCIAL APPLICATION: Implementation of resampling methods in
a modern interactive statistical language and graphics system as S-PLUS
will find a ready market in all areas of statistical application.
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