D. R. Brillinger ¹, H.K.Preisler ² and H. M. Naderi
_{¹Statistics Department
University of California
Berkeley, CA, 94720-3860
² Pacific Southwest Research Station
USDA Forest Service
Albany, CA, 94710}

Abstract
Forest fires are an important societal problem in many countries and regions. They cause extensive damage and sub-stantial funds are spent preparing for and fighting them. This work applies methods of probabilistic risk assessment to estimate chances of fires at a future time given explanatory variables. One focus of the work is random effects models. Questions of interest include: Are random effects needed in the risk model? If yes, how is the analysis to be implimented? An exploratory data analysis approach is taken employing both fixed and random effects models for data concerning the state of Oregon during the years 1989-1996.

Keywords: biased sampling, false discovery rate, forest fires, generalized mixed model, penalized quasi-likelihood, risk

In: McRoberts, R. et al. (eds).  Proceedings of the joint meeting of The 6th International Symposium On Spatial Accuracy Assessment In Natural Resources and Environmental Sciences and The 15th Annual Conference of The International Environmetrics Society, June 28 – July 1 2004, Portland, Maine, USA.