Show simple item record

dc.contributor.authorKilinc, Betul Kan
dc.contributor.authorAsfha, Huruy Debessay
dc.date.accessioned2020-05-16T22:44:54Z
dc.date.available2020-05-16T22:44:54Z
dc.date.issued2019-12-07
dc.identifier.citationKan Kilinc, B., & Debessay Asfha, H. (2019). Penalized Splines Fitting for a Poisson Response Including Outliers. Pakistan Journal of Statistics and Operation Research, 15(4), 979-988en_US
dc.identifier.issn1816 2711
dc.identifier.urihttps://doi.org/10.18187/pjsor.v15i4.2943
dc.identifier.urihttps://soar.wichita.edu/handle/10057/17653
dc.description© Authors. Open access. This work is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.description.abstractThere have been various studies in the literature on investigating the relationship between a count response and several covariates. Most researchers study count variables and use traditional methods (i.e. generalized linear models- GLM). However, GLM is limited when dealing with outliers and nonlinear relationships. Generalized Additive Models (GAM) is an extension of GLM, where the assumptions on the link functions and components are additive and smooth, respectively. Our aim is to propose a flexible extension of GLM and demonstrate the usefulness and performance of GAMs for the analysis of Poisson data set including outliers in the response variable through extensive Monte Carlo Simulations and using three applications.en_US
dc.language.isoen_USen_US
dc.publisherPakistan Journal of Statistics and Operation Researchen_US
dc.relation.ispartofseriesPakistan Journal of Statistics and Operation Research;v.15:no.4
dc.subjectPoissonen_US
dc.subjectSpline estimationen_US
dc.subjectDevianceen_US
dc.subjectAdditiveen_US
dc.titlePenalized splines fitting for a poisson response including outliersen_US
dc.typeArticleen_US
dc.rights.holder© 2018 pjsor.comen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record