I have been away from writing on the blog, even my personal opinions on current research topics (OK, that's what almost all of this writing is) due to travel, deadlines, and other obligations. I do want to take an opportunity to announce that a new paper from the SEED research group co-authored by Dr. Francis and Behailu Bekera has just been accepted for publication in the journal Reliability Engineering and System Safety. I am very excited about this, because I enjoy reading articles from this journal, and have found this research community engaging and interesting in person, as well as on paper. I'll write a more "reflective" entry about this sometime later, but if you'd like to take a look at the paper, please find it here. We will be presenting an earlier version of this work as a thought piece at ESREL 2013. More on the conference paper closer to the date of the conference in October.
Tag: Decision Analysis
New SEED Group Paper w/JHU, TAMU Collaborators!
We have just published a paper, "Characterizing the Performance of the Conway-Maxwell Poisson Generalized Linear Model" in the journal Risk Analysis
Here is the abstract:
Count data are pervasive in many areas of risk analysis; deaths, adverse health outcomes, infrastructure system failures, and traffic accidents are all recorded as count events, for example. Risk analysts often wish to estimate the probability distribution for the number of discrete events as part of doing a risk assessment. Traditional count data regression models of the type often used in risk assessment for this problem suffer from limitations due to the assumed variance structure. A more flexible model based on the Conway-Maxwell Poisson (COM-Poisson) distribution was recently proposed, a model that has the potential to overcome the limitations of the traditional model. However, the statistical performance of this new model has not yet been fully characterized. This article assesses the performance of a maximum likelihood estimation method for fitting the COM-Poisson generalized linear model (GLM). The objectives of this article are to (1) characterize the parameter estimation accuracy of the MLE implementation of the COM-Poisson GLM, and (2) estimate the prediction accuracy of the COM-Poisson GLM using simulated data sets. The results of the study indicate that the COM-Poisson GLM is flexible enough to model under-, equi-, and overdispersed data sets with different sample mean values. The results also show that the COM-Poisson GLM yields accurate parameter estimates. The COM-Poisson GLM provides a promising and flexible approach for performing count data regression.
Enjoy!