# The Problem with Parameter Redundancy Diana Cole, University of Kent.

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The Problem with Parameter Redundancy Diana Cole, University of Kent

Parameter Redundancy

Problems with Parameter Redundancy There will be a flat ridge in the likelihood of a parameter redundant model (Catchpole and Morgan, 1997), resulting in more than one set of maximum likelihood estimates. Numerical methods to find the MLE will not pick up the flat ridge, although could be picked up trying multiple starting values and looking at profile log-likelihoods. The Fisher information matrix will be singular (Rothenberg, 1971) and therefore the standard errors will be undefined. However the exact Fisher information matrix is rarely known. Standard errors are typically approximated using a Hessian matrix obtained numerically. Can parameter redundancy be detected from the standard errors?

Is example 1 parameter redundant? ParameterEstimateStandard Error 0.39imaginary 0.640.061 0.09imaginary 0.18imaginary

Is example 2 parameter redundant? ParameterEstimateStandard Error 0.410.70 0.830.07 0.100.11 0.190.33

Is example 3 parameter redundant? ParameterEstimateStandard Error 0.370.19 0.480.19 0.390.20 0.340.17 0.400.20 0.650.06 0.100.03 0.180.09

Simulation Study for Example 1/2 52% have defined standard errors ParameterTrue ValueAverage MLESt. Dev. MLE 0.40.570.27 0.70.500.29 0.10.500.31 0.20.520.30 SVD threshold%age SVD test correct 0.01100% 0.00172% 0.000111% 0.000012%

Computer Packages and Parameter Redundancy MARK (Cooch and Evans, 2014) Counts the number of estimable parameters using a numerical procedure involving a Single Value Decomposition, if “2ndPart” chosen rather than “Hessian” for variance estimation. Using “Hessian” method parameter redundancy is missed and agree with Cooch and Evans (2014)’s recommendation to use the default of “2ndPart”. Standard errors for non-identifiable parameters are either very large or zero and should be ignored. Parameter estimates for non-identifiable parameters are unreliable and should be ignored. Parameter redundancy could be caused by the model or the data. Recommend refitting any parameter redundant model with suitable constraints.

Computer Packages and Parameter Redundancy M-surge / E-surge (Choquet et al, 2004, Choquet et al, 2009) Uses the hybrid-symbolic-numeric method to detect parameter redundancy, but will not be able to tell whether parameter redundancy is caused by the model or the data. (Parameter redundancy caused by the model could be examined if you used simulated data.) Gives which parameters can and cannot be estimated, but cannot find estimable parameter combinations in parameter redundant models (currently only possibly symbolically) Also recommend refitting parameter redundant models with suitable constraints.

Conclusion It is not always possible to tell from model fitting that a model is parameter redundant. Recommend at least using numeric method to check parameter redundancy, but symbolic or hybrid methods are more reliable. Fitting parameter redundant models results in large bias for non-identifiable parameters and can introduce bias in the identifiable parameter models. If a model is parameter redundant it needs to be (re)fitted with constraints, which can be obtained using the symbolic method.

References Catchpole, E. A. and Morgan, B. J. T (1997) Detecting parameter redundancy. Biometrika, 84, 187-196. Choquet, R. and Cole, D.J. (2012) A Hybrid Symbolic-Numerical Method for Determining Model Structure. Mathematical Biosciences, 236, p117. Choquet, R., Reboulet, A.M., Pradel, R., Gimenez, O. Lebreton, J.D. (2004). M-SURGE: new software specifically designed for multistate capture- recapture models. Animal Biodiversity and Conservation 27(1): 207-215. Choquet, R., Rouan, L., Pradel, R. (2009). Program E-SURGE: a software application for fitting Multievent models. Series: Environmental and Ecological Statistics, Vol. 3 Thomson, David L.; Cooch, Evan G.; Conroy, Michael J. (Eds.) p 845-865. Cole, D.J., Morgan, B.J.T., Titterington, D.M. (2010) Determining the Parametric Structure of Non-Linear Models. Mathematical Biosciences, 228, 16-30. Cooch and Evans (2014) Program Mark. A Gentle Introduction. Rothenberg, T.J. (1971) Identification in parametric models. Econometrica, 39, 577-591. Viallefont, A., Lebreton, J.D., Reboulet, A.M. and Gory, G. (1998) Parameter Identifiability and Model Selection in Capture-Recapture Models: A Numerical Approach. Biometrical Journal, 40, 313-325.

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