Solving problems in parameter redundancy using computer algebra
E. A. Catchpole, Byron J. T. Morgan, Anne Viallefont · Journal of Applied Statistics · 2002
A model, involving a particular set of parameters, is said to be parameter redundant when the likelihood can be expressed in terms of a smaller set of parameters. In many important cases, the parameter redundancy of a model can be checked by evaluating the symbolic rank of a derivative matrix. We describe the main results, and show how to construct this matrix using the symbolic algebra package Maple. We apply the theory to examples from the mark-recapture field. General code is given which can be applied to other models.