Random Variate Generation for Exponential and Gamma Tilted Stable Distributions
Random Variate Generation for Exponential and Gamma Tilted Stable Distributions
复制标题
指数和伽玛倾斜稳定分布的随机变量生成
DOI：
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发表时间：
2021
影响因子：
0.9
通讯作者：
Hongbiao Zhao
中科院分区：
文献类型：
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作者：
Yan Qu;A. Dassios;Hongbiao Zhao
我们开发了一个新的有效的模拟计划抽样两个家庭的倾斜稳定分布：指数倾斜稳定（ETS）和伽玛倾斜稳定（GTS）分布。我们的计划是基于二维单拒绝。对于ETS族，它的复杂性在所有参数范围内一致有界。这种新算法优于所有现有方案。特别是，它是更有效的比著名的双重拒绝计划，这是唯一的算法，一致有界的复杂性，我们可以在目前的文献中找到。除了ETS家庭，我们的计划也是灵活的，可以进一步扩展生成的GTS家庭，这不能很容易地通过扩展双拒绝计划。我们的算法是简单的实现，并进行了数值实验和测试，以证明的准确性和效率。
We develop a new efficient simulation scheme for sampling two families of tilted stable distributions: exponential tilted stable (ETS) and gamma tilted stable (GTS) distributions. Our scheme is based on two-dimensional single rejection. For the ETS family, its complexity is uniformly bounded over all ranges of parameters. This new algorithm outperforms all existing schemes. In particular, it is more efficient than the well-known double rejection scheme, which is the only algorithm with uniformly bounded complexity that we can find in the current literature. Beside the ETS family, our scheme is also flexible to be further extended for generating the GTS family, which cannot easily be done by extending the double rejection scheme. Our algorithms are straightforward to implement, and numerical experiments and tests are conducted to demonstrate the accuracy and efficiency.