论文标题
样品路径大偏差，用于一类重尾马尔可夫添加剂过程
Sample-path large deviations for a class of heavy-tailed Markov additive processes
论文作者
论文摘要
对于由仿射递归驱动的一类添加剂过程$ x_ {n + 1} = a_n x_n + b_n $，我们在$ d [0,1] $的$ M_1'$ topology中开发了一个示例路径大偏差原理。我们允许$ b_n $具有标志，并专注于凯斯滕（Kesten）条件持有$ a_1 $的情况，从而导致重型分布。我们较大的偏差结果中最有可能的路径是阶梯函数，既有正跳跃和负跳。
For a class of additive processes driven by the affine recursion $X_{n+1} = A_n X_n + B_n$, we develop a sample-path large deviations principle in the $M_1'$ topology on $D [0,1]$. We allow $B_n$ to have both signs and focus on the case where Kesten's condition holds on $A_1$, leading to heavy-tailed distributions. The most likely paths in our large deviations results are step functions with both positive and negative jumps.
