协方差表示和一些的连贯措施
1School of Statistics and Data Science, Qufu Normal University, Qufu 273165, China.
Entropy (Basel, Switzerland)
|November 24, 2023
概括
这项研究引入了新的度,基于尾部的累积余 Tsallis (TCRTE) 和基于尾部的右尾偏差 (TRTD),以及在统计分布中的应用. 这些输入量为高级风险分析提供上限和连贯性质.
科学领域:
- 信息理论 信息理论
- 统计建模 统计建模
- 可能性分布的概率分布.
背景情况:
- 在统计分析中,对于量化不确定性和风险至关重要.
- 现有的度措施可能无法完全捕捉依赖尾部的风险.
- 协方差和Choquet积分表示提供了分析性质的框架.
研究的目的:
- 为了获得特定值的共变性和Choquet积分表示.
- 建立上限并讨论这些的连贯性质.
- 为增强风险评估提出基于尾部的新措施.
主要方法:
- 获得了对所选输入的共变量和Choquet积分表示.
- 为这些输入值推导了上限.
- 引入了基于尾部的累积余 Tsallis 序α 的 (TCRTE) 和基于尾部的右尾偏差 (TRTD).
- 已定义的累积余 Tsallis (CRTES) 缺陷和右尾偏差 (RTDS) 的缺陷.
主要成果:
- 对于新定义的缺陷输入,建立了同等结果.
- 通过对圆,逆高斯分布,马分布和β分布的模拟来证明CRTES的应用.
- 提供了上限,并讨论了所讨论的的连贯性质.
结论:
- 拟议的基于尾部的度 (TCRTE,TRTD,CRTES,RTDS) 为风险分析提供了有价值的工具,特别是捕捉尾部行为.
- 协变量和Choquet积分表示为这些测量提供了理论基础.
- 模拟证实了CRTES对各种连续概率分布的实用性.
相关概念视频
Entropy and the Second Law of Thermodynamics
2.8K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
The relation between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
2.8K
Entropy Change in Reversible Processes
2.6K
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.
2.6K
Entropy
30.2K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
30.2K
Entropy and Solvation
7.1K
The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
7.1K
Variability: Analysis
143
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
143
Propagation of Uncertainty from Random Error
699
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
699


