| Description |
Sequential Experimental Designs for Generalized Linear Models
Experimental Design is about choosing locations in which to take measurements. For example, choosing different drug doses in which to examine the success of a treatment. A lot has been written on experimental design for statistical linear models. But, often these models do not describe the problem well enough. Common examples are when the response is binary (?success? or ?failure?) or when the response is discrete count data (fitting a Poisson model). Analysis of such data is familiar through Generalized Linear Models (GLM). The files attached give tools for designing sequential GLM experiments.
Unlike one-stage experimental plans, that require the researcher to fix in advance the factor settings at which data will be observed, sequential experimental design allows updating and improving the experimental plan following the data already observed.
If you are interested in one-stage plans, algorithms for their implementation were uploaded to MATLAB central file-exchange separately.
For understanding you may want to read:
Hovav A. Dror and David M. Steinberg (2006). ?Sequential Experimental Design for Generalized Linear Models,? Technical Report RP-SOR-0607, Tel Aviv University. Available at: http://www.math.tau.ac.il/~dms/GLM_Design
This file contains:
Source code and examples for:
* Sensitivity Tests (one factor, binary response, fully sequential; such as "dose response"): SensitivityTest.m, SensitivityTestAUTO.m, Screenshot: SensitivityTestScreenshot.jpg
* Extension to multivariate cases: MultivariateTest.m, MultivariateTestAUTO.m
* Extension to Group Sequential multivariate design: GroupSequential.m, GroupSequentialAUTO.m
* Extension for a Poisson response model, with different possible models (with and without interactions): TwoModelsAndPoissonSequential.m, TwoModelsAndPoissonSequentialAuto.m
Bayesian analysis is utilized within all these files, but examples that focus on this issue will be uploaded separately.
More details and latest version is available at: http://www.math.tau.ac.il/~dms/GLM_Design |
