Paper Info
Title
Efficient estimation of COM-Poisson regression and a generalized additive model.
Abstract
The Conway–Maxwell–Poisson (CMP) or COM–Poisson regression is a popular model for count data due to its ability to capture both under dispersion and over dispersion. However, CMP regression is limited when dealing with complex nonlinear relationships. With today’s wide availability of count data, especially due to the growing collection of data on human and social behavior, there is need for count data models that can capture complex nonlinear relationships. One useful approach is additive models; but, there has been no additive model implementation for the CMP distribution. To fill this void, we first propose a flexible estimation framework for CMP regression based on iterative reweighed least squares (IRLS) and then extend this model to allow for additive components using a penalized splines approach. Because the CMP distribution belongs to the exponential family, convergence of IRLS is guaranteed under some regularity conditions. Further, it is also known that IRLS provides smaller standard errors compared to gradient-based methods. We illustrate the usefulness of this approach through extensive simulation studies and using real data from a bike sharing system in Washington, DC.
Year
DOI
Venue
2018
10.1016/j.csda.2017.11.011
Computational Statistics & Data Analysis
Keywords
Field
DocType
IRLS,Penalized splines,P-IRLS,Over and under dispersion,Time series
Convergence (routing),Least squares,Overdispersion,Mathematical optimization,Regression,Additive model,Exponential family,Count data,Statistics,Generalized additive model,Mathematics
Journal
Volume
ISSN
Citations 
121
0167-9473
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Suneel Chatla101.35
Galit Shmueli226523.00