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Estimation in parameter-redundant models. (English) Zbl 0938.62012
Summary: The likelihood surface resulting from a parameter-redundant stochastic model is maximized along a completely flat ridge. This ridge may be orthogonal to some parameter axes, so that these parameters have unique maximum likelihood estimates. For exponential-family models, we show how to determine which parameter combinations are estimable. The approach requires the calculation of a derivative matrix and the determination of its null space, both of which are readily achieved in computer algebra packages. Illustrative examples are drawn from the areas of compartment modelling and ring-recovery analysis.

62F10Point estimation
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