The above equations suggest that the unknown parameters in polynomials A( ) and B() can be estimated with RLS with the transformed variables yn and un k. Having polynomials A( ) and B(-) we can go back to Equation 13.1 and obtain an estimate of the error term, e , as [Pg.224]

Many workers do not transform the parameter estimates back to the original coordinate system, but instead work with the parameter estimates obtained in the coded factor space. This can often lead to surprising and seemingly contradictory results. As an example, the fitted model in the coded factor space was found to be [Pg.240]

A necessary and sufficient condition for identifia-bility is the concept that with p-estimable parameters at least p-solvable relations can be generated. Traditionally, for linear compartment models this involves using Laplace transforms. For example, going back to the 1-compartment model after first-order absorption with complete bioavailability, the model can be written in state-space notation as [Pg.32]

Thus, it is the algebraic effect of coding and the numerical values of the estimated parameters that cause some terms to be added to the model (e.g., P2) and other terms to disappear from the model (e.g., P,). Transformation of parameter estimates back to the original factor space usually avoids possible misinterpretation of results. [Pg.243]

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