| Statistical Programs |
| College of Agriculture | University of Idaho |
| Seminar Announcement |
| "Applied Statistics in Agriculture" |
|
Alternative Procedures for the Estimation of Nonlinear Regression Parameters Presented By |
| William J. Price |
|
Statistical Programs College of Agriculture |
| Tuesday, March 25 3:30 P. M. Ag. Science 62 |
|
Biological research data are often represented using
nonlinear model specifications that lend themselves to
the testing of relevant hypotheses concerning the model
parameters. This is typically achieved with classical
nonlinear least squares techniques such as Gauss-Newton or
Levenberg-Marquardt which allow for both the estimation and
nference phases of the analysis. Under some circumstances,
however, sensitivity to data or model specifications may
lead these methods to fail convergence tests or exhibit
nonlinearity in the parameter estimates, which will in turn
limit the usefulness of inferential results. In such cases,
other estimation methods may present a means of avoiding
these problems while providing analogous results. The
genetic algorithm combined with bootstrapping and Bayesian
estimation are two such alternatives. Genetic algorithms
represent a nonparametric approach which, when augmented with
bootstrap methods, result in both parameter estimation and
approximation of the distribution(s). Bayesian estimation,
on the other hand, leads directly to parameter distribution
and achieves the required moments. These methods and classical
nonlinear least squares are demonstrated using a four-parameter
cumulative Weibull function fitted to onion seed germination data.
All interested faculty, staff, and graduate students are invited to attend. |
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