Algorithm 717: Subroutines for maximum likelihood and quasi-likelihood estimation of parameters in nonlinear regression models Export

@article{citeulike:5054270, abstract = {We present FORTRAN 77 subroutines that solve statistical parameter estimation problems for general nonlinear models, e.g., nonlinear least-squares, maximum likelihood, maximum quasi-likelihood, generalized nonlinear least-squares, and some robust fitting problems. The accompanying test examples include members of the generalized linear model family, extensions using nonlinear predictors (\“nonlinear GLIM\”), and probabilistic choice models, such as linear-in-parameter multinomial probit models. The basic method, a generalization of the NL2SOL algorithm for nonlinear least-squares, employs a model/trust-region scheme for computing trial steps, exploits special structure by maintaining a secant approximation to the second-order part of the Hessian, and adaptively switches between a Gauss-Newton and an augmented Hessian approximation. Gauss-Newton steps are computed using a corrected seminormal equations approach. The subroutines include variants that handle simple bounds on the parameters, and that compute approximate regression diagnostics.}, address = {New York, NY, USA}, author = {Bunch, David S. and Gay, David M. and Welsch, Roy E.}, citeulike-article-id = {5054270}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=151279}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/151271.151279}, doi = {10.1145/151271.151279}, issn = {0098-3500}, journal = {ACM Trans. Math. Softw.}, keywords = {algorithm, nonlinear, optimization, p\_my\_db\_cul}, number = {1}, pages = {109--130}, posted-at = {2009-07-04 08:40:20}, priority = {2}, publisher = {ACM}, title = {Algorithm 717: Subroutines for maximum likelihood and quasi-likelihood estimation of parameters in nonlinear regression models}, url = {http://dx.doi.org/10.1145/151271.151279}, volume = {19}, year = {1993} }

TY - JOUR ID - citeulike:5054270 L3 - citeulike-article-id:5054270 N2 - We present FORTRAN 77 subroutines that solve statistical parameter estimation problems for general nonlinear models, e.g., nonlinear least-squares, maximum likelihood, maximum quasi-likelihood, generalized nonlinear least-squares, and some robust fitting problems. The accompanying test examples include members of the generalized linear model family, extensions using nonlinear predictors (“nonlinear GLIM”), and probabilistic choice models, such as linear-in-parameter multinomial probit models. The basic method, a generalization of the NL2SOL algorithm for nonlinear least-squares, employs a model/trust-region scheme for computing trial steps, exploits special structure by maintaining a secant approximation to the second-order part of the Hessian, and adaptively switches between a Gauss-Newton and an augmented Hessian approximation. Gauss-Newton steps are computed using a corrected seminormal equations approach. The subroutines include variants that handle simple bounds on the parameters, and that compute approximate regression diagnostics. IS - 1 JF - ACM Trans. Math. Softw. SN - 0098-3500 AD - New York, NY, USA EP - 130 TI - Algorithm 717: Subroutines for maximum likelihood and quasi-likelihood estimation of parameters in nonlinear regression models PB - ACM VL - 19 SP - 109 KW - algorithm KW - nonlinear KW - optimization KW - p_my_db_cul AU - Bunch, David AU - Gay, David AU - Welsch, Roy PY - 1993/// UR - http://dx.doi.org/10.1145/151271.151279 ER -