When studying the empirical phenomenon of wildfires, we can distinguish between the occurrence at a specific location and time and the burnt area measured. This study proposes using structured additive regression models based on zero-one-inflated beta distribution for studying wildfire occurrence and burnt area simultaneously. Beta distribution affords a convenient way of studying the percentage of burnt area in cases where such percentages are bounded away from zero and one. Inflation with zeros and ones enables observations without wildfires or with 100% burnt areas to be treated as special cases. Structured additive regression allows one to include a variety of covariates, while simultaneously exploring spatial and temporal correlations. Our inferences are based on an efficient Markov chain Monte Carlo simulation algorithm utilizing iteratively weighted least squares approximations as proposal densities. Application of the proposed methodology to a large wildfire database covering Galicia (Spain) provides essential information for improved wildfire management.

在研究野火的经验现象时，我们可以区分在特定位置和时间发生的事件与测得的燃烧面积。这项研究提出使用基于零一膨胀的β分布的结构化加性回归模型来同时研究野火的发生和燃烧面积。Beta分布提供了一种方便的方法来研究燃烧面积的百分比（如果这些百分比的边界远离零和一）。零和一的通货膨胀使得没有野火或燃烧面积为100％的观测被视为特殊情况。结构化加性回归允许同时包含各种协变量，同时探索空间和时间相关性。我们的推论基于有效的马尔可夫链蒙特卡罗模拟算法，该算法利用迭代加权最小二乘近似作为提案密度。拟议的方法在覆盖加利西亚（西班牙）的大型野火数据库中的应用为改进野火管理提供了必要的信息。