| Name : R-brglm2
| |
| Version : 0.9.2
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp155.1.1
| Date : 2023-10-12 09:38:14
|
| Group : Development/Libraries/Other
| Source RPM : R-brglm2-0.9.2-lp155.1.1.src.rpm
|
| Size : 0.72 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Bias Reduction in Generalized Linear Models
|
Description :
Estimation and inference from generalized linear models based on
various methods for bias reduction and maximum penalized likelihood
with powers of the Jeffreys prior as penalty. The \'brglmFit\' fitting
method can achieve reduction of estimation bias by solving either the
mean bias-reducing adjusted score equations in Firth (1993)
< doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009)
< doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted
score equations in Kenne et al. (2017) < doi:10.1093/biomet/asx046>, or
through the direct subtraction of an estimate of the bias of the
maximum likelihood estimator from the maximum likelihood estimates as
in Cordeiro and McCullagh (1991)
< https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020)
< doi:10.1007/s11222-019-09860-6> for more details. Estimation in all
cases takes place via a quasi Fisher scoring algorithm, and S3 methods
for the construction of of confidence intervals for the reduced-bias
estimates are provided. In the special case of generalized linear
models for binomial and multinomial responses (both ordinal and
nominal), the adjusted score approaches to mean and media bias
reduction have been found to return estimates with improved frequentist
properties, that are also always finite, even in cases where the
maximum likelihood estimates are infinite (e.g. complete and
quasi-complete separation; see Kosmidis and Firth, 2020
< doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in
logistic regression).
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.5/x86_64 |
