论文标题
积极变量的随机总和的自动回归模型：将树生长应用于气候和昆虫暴发的函数
Autoregressive models for time series of random sums of positive variables: application to tree growth as a function of climate and insect outbreaks
论文作者
论文摘要
我们为正变量的随机总和介绍了一系列的半参数模型。我们的方法允许总和内的术语数量变化，因此非常适合自然科学中遇到的许多例子。我们研究模型的稳定性特性，并提供有效的统计推理程序来估计模型参数。结果表明，所提出的准最大可能性估计量是一致的，并且在渐近地正态分布。 This work is complemented by simulation results and applied to annual growth rate time series of white spruce (Picea glauca) trees from a few dozen sites in Quebec spanning 41 years, including one major spruce budworm (Choristoneura fumiferana) outbreak from around 1968 to 1991. We found significant growth reductions due to budworm-induced by defoliation up to two years in the past.我们的结果还揭示了夏季最高温度，降水量和气候水分指数的积极影响，以及春季气候水分指数的负面影响以及前夏天的最高温度。但是，考虑到气候和增长落叶的相互作用并不能改善该模型在该数据集上的性能。这项研究代表了一个重大进展，我们的结果代表了对气候和昆虫落叶对气候变化时树木生长的综合作用的有用工具，在这种情况下，预计频率和暴发的频率和严重程度以及预计温度的升高。
We present a broad class of semi-parametric models for time series of random sums of positive variables. Our methodology allows the number of terms inside the sum to be time-varying and is therefore well suited to many examples encountered in the natural sciences. We study the stability properties of the models and provide a valid statistical inference procedure to estimate the model parameters. It is shown that the proposed quasi-maximum likelihood estimator is consistent and asymptotically normally distributed. This work is complemented by simulation results and applied to annual growth rate time series of white spruce (Picea glauca) trees from a few dozen sites in Quebec spanning 41 years, including one major spruce budworm (Choristoneura fumiferana) outbreak from around 1968 to 1991. We found significant growth reductions due to budworm-induced by defoliation up to two years in the past. Our results also revealed positive effects of maximum temperature, precipitation and the climate moisture index in the summer, as well as negative effects of the climate moisture index in the spring and the maximum temperature in the previous summer. However, considering the interaction of climate and defoliation on growth did not improve the model's performance on this dataset. This study represent a major advances and our result represent an useful tool in the understanding of the combined effects of climate and insect defoliation on tree growth in the face of climate change, where the frequency and the severity of outbreaks, as well as an increase of temperature is expected.
