论文标题
重型模型中的加权最大值和非平稳随机长度序列的总和
Weighted maxima and sums of non-stationary random length sequences in heavy-tailed models
论文作者
论文摘要
加权的非平稳随机长度序列的总和和最大变化的随机变量的总和可能具有相同的尾巴和极端指数，Markovich和Rodionov（2020）。 主要的限制是，在具有最小尾部索引的系列方案中存在一个独特的系列，术语数的尾巴比术语的尾巴较轻，而权重为正常数。这些假设在此处进行了更改：允许有界的随机数具有最小的尾部索引，术语编号的尾巴可能比术语的尾巴重，并且权重可以实现。然后，我们在新假设下得出了加权非平稳随机长度序列的尾巴和极端指标。
The sums and maxima of weighted non-stationary random length sequences of regularly varying random variables may have the same tail and extremal indices, Markovich and Rodionov (2020). The main constraints are that there exists a unique series in a scheme of series with the minimum tail index, the tail of the term number is lighter than the tail of the terms and the weights are positive constants. These assumptions are changed here: a bounded random number of series is allowed to have the minimum tail index, the tail of the term number may be heavier than the tail of the terms and the weights may be real-valued. Then we derive the tail and extremal indices of the weighted non-stationary random length sequences under the new assumptions.
