RT Journal Article
T1 Generalized Hermite Distribution Modelling with the R Package hermite
A1 Moriña, David
A1 Higueras, Manuel
A1 Puig, Pedro
A1 Oliveira Pérez, María
AB The Generalized Hermite distribution (and the Hermite distribution as a particular case) is often used for fitting count data in the presence of overdispersion or multimodality. Despite this, to our knowledge, no standard software packages have implemented specific functions to compute basic probabilities and make simple statistical inference based on these distributions. We present here a set of computational tools that allows the user to face these difficulties by modelling with the Generalized Hermite distribution using the R package hermite. The package can also be used to generate random deviates from a Generalized Hermite distribution and to use basic functions to compute probabilities (density, cumulative density and quantile functions are available), to estimate parameters using the maximum likelihood method and to perform the likelihood ratio test for Poisson assumption against a Generalized Hermite alternative. In order to improve the density and quantile functions performance when the parameters are large, Edgeworth and Cornish-Fisher expansions have been used. Hermite regression is also a useful tool for modeling inflated count data, so its inclusion to a commonly used software like R will make this tool available to a wide range of potential users. Some examples of usage in several fields of application are also given
PB R Foundation for Statistical Computing
SN 2073-4859
YR 2015
FD 2015
LK http://hdl.handle.net/10347/24321
UL http://hdl.handle.net/10347/24321
LA eng
NO David Moriña, Manuel Higueras, Pedro Puig and María Oliveira , The R Journal (2015) 7:2, pages 263-274
NO This work was partially funded by the grant MTM2012-31118, by the grant UNAB10-4E-378 co-funded by FEDER “A way to build Europe” and by the grant MTM2013-41383P from the Spanish Ministry of Economy and Competitiveness co-funded by the European Regional Development Fund (EDRF)
DS Minerva
RD 22 abr 2026