1 / 11

The Problem with Parameter Redundancy

The Problem with Parameter Redundancy. Diana Cole, University of Kent. Parameter Redundancy. A model is parameter redundant (or non-identifiable) if you cannot estimate all the parameters.

yahto
Download Presentation

The Problem with Parameter Redundancy

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. Parameter Redundancy • A model is parameter redundant (or non-identifiable) if you cannot estimate all the parameters. • Consider a basic occupancy models which considers whether or not a species is present at a particular site. • Parameters: – species is detected. • Species detected at a site with probability . • Species not detected at a site with probability • Basic model is parameter redundant – can only estimate rather than and . • There are several different methods for detecting parameter redundancy, including • numerical methods (egViallefont et al, 1998) • symbolic methods (eg Cole et al, 2010) • hybrid symbolic-numeric method (Choquet and Cole, 2012) • Generally involves calculating the rank of a matrix, which gives the number of parameters that can be estimated.

  3. 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?

  4. Is example 1 parameter redundant? • Hessian (H) computed numerically has rank 4 (exact Hessian would have rank < 4 if parameter redundant) • Single Value Decomposition • Write , Matrix is diagonal matrix (Eigen values), the number of non-zero values is the rank of the matrix. • Standardised • Hybrid-Symbolic Numeric method: rank 3, only is estimable. • Symbolic Method: rank 3, estimable parameter combinations

  5. Is example 2 parameter redundant? • Hessian (H) computed numerically has rank 4 (exact would have rank < 4 if parameter redundant) • Standardised Single Value Decomposition • Hybrid-Symbolic Numeric method: rank 3, only is estimable. • Symbolic Method: rank 3, estimable parameter combinations

  6. Is example 3 parameter redundant? • Standardised Single Value Decomposition [1.00 0.65 0.11 0.096 0.074 0.039 0.034 0.0011] • Hybrid-Symbolic Numeric method: rank 8 so is not parameter redundant. • Symbolic model: rank 8 so is not parameter redundant, but further test reveal that model could be near redundant, as when model is same as example 1.

  7. Simulation Study for Example 1/2 52% have defined standard errors

  8. 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.

  9. 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.

  10. 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.

  11. 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.

More Related