Author: Lynn R. LaMotte
Abstract:
This project focused on estimating postmortem interval (PMI)
using insect evidence, because this field provides the largest data set for
model exploration; however, the statistical methods used are broadly applicable
in all scientific disciplines concerned with PMI estimation.
Inverse prediction (IP) is a statistical methodology that
can be used to obtain confidence sets on estimates. This project's objective
was to extend and adapt methods of IP for use in forensically important
settings.
The three main parts of this research were to develop
methods of IP multivariate quantitative responses with heterogeneous
variance-covariance structure, including flexible, adaptive modeling of both
means and variances; to develop methods of IP for categorical responses with
the same modeling flexibility; and to integrate these results to create IP
methods for hybrid quantitative-categorical responses.
A common objective in this three-pronged effort was to
assess and quantify the inherent uncertainty accurately and defensibly. The
over-arching objective was to develop a set of statistical methods and
versatile, accessible tools for IP in settings important in assessing time
since death and other forensically important applications.
This objective was accomplished by making it possible for IP
methodology to be implemented within the broad context of mixed linear models.
Its computations leading to p-values and confidence sets can be more easily
performed, because an investigator can now do the analysis using a variety of
widely available statistical computing packages.
The project has submitted for publication scientific
manuscripts to illustrate the immediate practical value of the IP methods for
guiding PMI research design.
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