Estimation (GLE models)

Method Select the maximum likelihood estimation method to be used; the available options are:
  • Fisher scoring
  • Newton-Raphson
  • Hybrid

Maximum Fisher iterations Specify a non-negative integer. A value of 0 specifies the Newton-Raphson method. Values greater than 0 specify to use the Fisher scoring algorithm up to iteration number n, where n is the specified integer, and Newton-Raphson thereafter.

Scale parameter method Select the method for the estimation of the scale parameter; the available options are:
  • Maximum likelihood estimate
  • Fixed value. You also set the Value to be used.
  • Deviance
  • Pearson Chi-square
Negative Binomial method Select the method for the estimation of the negative binomial ancillary parameter; the available options are:
  • Maximum likelihood estimate
  • Fixed value. You also set the Value to be used.

Perform non negative least squares. Select this option to perform non-negative least squares (NNLS) estimation. NNLS is a type of constrained least squares problem where the coefficients are not allowed to become negative. Not all data sets are suitable for NNLS, which requires a positive or no correlation between predictors and target.

Parameter Convergence Convergence is assumed if the maximum absolute change or maximum relative change in the parameter estimates is less than the value specified, which must be non-negative. The criterion is not used if the value specified equals 0.

Log-Likelihood Convergence Convergence is assumed if the absolute change or relative change in the log-likelihood function is less than the value specified, which must be non-negative. The criterion is not used if the value specified equals 0.

Hessian Convergence For the Absolute specification, convergence is assumed if a statistic based on the Hessian is less than the value specified. For the Relative specification, convergence is assumed if the statistic is less than the product of the value specified and the absolute value of the log-likelihood. The criterion is not used if the value specified equals 0.

Maximum iterations You can specify the maximum number of iterations the algorithm will execute. The algorithm uses a doubly iterative process that consists of an inner loop and an outer loop. The value that is specified for the maximum number of iterations applies to both loops. Specify a non-negative integer. The default is 100.

Singularity tolerance This value is used as the tolerance in checking singularity. Specify a positive value.

Note: By default, Parameter Convergence is used, where the maximum Absolute change at a tolerance of 1E-6 is checked. This setting might produce results that differ from the results that are obtained in versions before version 17. To reproduce results from pre-17 versions, use Relative for the Parameter Convergence criterion and keep the default tolerance value of 1E-6.