极端价值统计中的罕见事件,使用动力尾巴的跳跃过程的统计数据
Alberto Bassanoni1,2, Alessandro Vezzani1,3, Raffaella Burioni1,2
1Dipartimento di Scienze Matematiche, Fisiche ed Informatiche, Università degli Studi di Parma, Parco Area delle Scienze 7/A, 43124 Parma, Italy.
Chaos (Woodbury, N.Y.)
|August 30, 2024
概括
我们使用大跃进原理分析随机过程中的罕见事件. 动力尾流程中的大波动是由单个,巨大的跳跃驱动的,影响极端值统计数据.
科学领域:
- 统计物理 统计物理
- 随机过程 随机过程
- 极端价值理论 极端价值理论
背景情况:
- 在自然界中,具有功率尾跳跃分布的随机过程是常见的.
- 了解罕见事件对于各种领域的风险评估至关重要.
- 大跃进原理解释了这些过程中的大波动.
研究的目的:
- 为了分析极端值的罕见事件,分析随机对称跳跃过程的统计数据.
- 为了确定特定的Lévy过程的罕见事件的概率分布的分析形式.
- 调查过程拓学对极端值统计学的影响.
主要方法:
- 大跃进原理的应用.
- 对罕见事件的概率分布的分析确定.
- 广泛的数值模拟用于验证.
主要成果:
- 证实大波动是通过单一的宏观跳跃实现的.
- 对于Lévy航班的分析结果,延长了它们的有效期.
- 证明了Lévy-Lorentz气体中的格子拓会诱导影响极端值统计的记忆效应.
结论:
- 大跃进原理提供了一个强大的工具,用于理解动力尾随随机过程中的罕见事件.
- 该研究提供了对Lévy航班,Lévy步行和Lévy-Lorentz气体极端值统计的分析见解.
- 这些发现对化学,气候学,金融和生态学等多个领域都有影响.
相关概念视频
Parametric Survival Analysis: Weibull and Exponential Methods
390
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
390
Unusual Results
3.2K
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...
3.2K
Poisson Probability Distribution
7.8K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
The...
7.8K
Probability Histograms
11.1K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.1K
Probability Distributions
6.8K
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.8K
Entropy Change in Reversible Processes
2.5K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.5K


