  We propose an algorithm to select parameter subset combinations that can be
estimated using an ordinary least-squares (OLS) inverse problem formulation
with a given data set. First, the algorithm selects the parameter combinations
that correspond to sensitivity matrices with full rank. Second, the algorithm
involves uncertainty quantification by using the inverse of the Fisher
Information Matrix. Nominal values of parameters are used to construct
synthetic data sets, and explore the effects of removing certain parameters
from those to be estimated using OLS procedures. We quantify these effects in a
score for a vector parameter defined using the norm of the vector of standard
errors for components of estimates divided by the estimates. In some cases the
method leads to reduction of the standard error for a parameter to less than
1\% of the estimate.
