Grant Details
Grant Number: |
5R29CA072015-03 Interpret this number |
Primary Investigator: |
Sherman, Michael |
Organization: |
Texas A&M University System |
Project Title: |
Subsampling Methods for Spatial and Longitudinal Data |
Fiscal Year: |
1998 |
Abstract
DESCRIPTION (Adapted from applicant's abstract): Data which are temporally
or spatially close are often correlated. Common examples include data on a
patient collected over time or environmental and demographic variables
measured at locations in close spatial proximity. The correlation between
neighboring sights makes analysis of summary statistics more difficult than
for independent data, and for this reason subsampling procedures are
attractive in this setting. The proposed research seeks to broaden the
applicability of omnibus subsampling methods in the area of longitudinal and
spatial statistics. Specifically, distribution theory (asymptotic
normality) and variance estimation for general (potentially complicated)
statistics computed from spatial data will be addressed, as will the problem
of obtaining accurate confidence intervals for unknown parameters. The
basic subsampling idea in the dependent data setting is to compute the
statistics of interest on smaller subseries (longitudinal data) or subshapes
(spatial data) as "replicates" of the original statistics which retain the
original dependence structure of the data. These replicates will be used to
obtain (under appropriate conditions) asymptotic normality, variance
estimation (convergence results and deriving effective choices of subseries,
subshape size), and to obtain accurate confidence intervals by "mixing" the
empirical distribution of the replicates with that of the limiting normal
distribution.
The methodology is sufficiently general and will be extended to handle other
data structures. For example, the methods (with modifications) will also be
applied to statistics derived from estimation equations, which have direct
applications to longitudinal studies, clinical trials, as well as studying
relationships between spatially located variables; e.g., prevalence of
disease.
Publications
None