DESCRIPTION: This proposal addresses modeling and estimation problems that
arise with missing data and/or mismeasured variables. The project consists
of three major areas: Frequentist Multiple Imputation Procedures}: The
research will consider parametric, nonparametric and semiparametric
approaches. The problem to be addressed include proposing new methods of
imputation, deriving the properties of the resulting estimators, proposing a
method to compare the efficiencies among the procedures and developing
inference procedures. The goals are (1) providing the most efficient
multiple imputation procedure for parametric and semiparametric models and
(2) developing inference procedures for the less efficient but most easily
implemented estimators.
Measurement error problems in mixed effects pharmacokinetics models: The
goals for this research area include (1) determining the effect of
measurement errors for both classical measurement error models and the
Berkson type of measurement error models, (2) developing graphical tools
which can be used to detect the severity of the measurement error effects
and (3) providing methods which adjust for the measurement errors for both
the classical and the clinical pharmacokinetics models. The study will
contain theoretical asymptotic analysis supplemented by extensive
simulations and real-data applications.
Resampling methods in data-driven smoothing parameter determination: This
research will generalize the bootstrap method of Wang (1996) for
semi-parametric heteroscedastic regression models to semiparametric
procedures which analyze data with missing/mismeasured variables. Because
of the technical difficulties, the smoothing parameters for most of the
semiparametric procedures have often been determined by ad hoc methods. The
approaches proposed will be easy to implement and estimate the smoothing
parameters in an automatic fashion. The success of this research will help
to promote the use of semiparametric procedures.
Error Notice
The database may currently be offline for maintenance and should be operational soon. If not, we have been notified of this error and will be reviewing it shortly.
We apologize for the inconvenience.
- The DCCPS Team.
