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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...
Helps choose a Box-Cox power transformation for a multivariate linear regression.
Assume you are looking at the residuals of [b,bint,r] = regress(y,X) and it seems a transformation is in place. Use:
boxcoxlm(y,X) to find the best...
Bayesian analysis is often hard to implement. While working on sequential experimental design for GLM, we suggested a simple implementation that numerically assesses the posterior in cases where the closed form of the Bayesian posterior is hard to...
Optimal experimental designs for generalized linear models (GLM) depend on the unknown coefficients, and two experiments having the same model but different coefficient values will typically have different optimal designs. Therefore, unlike...
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