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相关概念视频

Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Probability Distributions01:32

Probability Distributions

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 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.
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Overview
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Random Variables01:09

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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Randomized Experiments01:13

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
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  1. 首页
  3. 使用设定值概率的概念进行通用的强大优化
  1. 首页
  3. 使用设定值概率的概念进行通用的强大优化

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An R-Based Landscape Validation of a Competing Risk Model
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使用设定值概率的概念进行通用的强大优化

Davide La Torre1, Franklin Mendivil2, Matteo Rocca3

  • 1SKEMA Business School, Université Côte d'Azur Sophia Antipolis Campus, Sophia Antipolis, France.

Journal of optimization theory and applications
|September 2, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

本研究引入了使用定值概率来估计不确定的概率的强有力的框架. 它在金融建模和风险管理方面提供了更好的决策和弹性.

关键词:
投资组合优化风险措施坚固性设定价值的概率指标

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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科学领域:

  • 数学统计
  • 金融数学
  • 决策理论

背景情况:

  • 概率的统计估计受到不确定性和未知值的挑战.
  • 当处理不准确的概率信息时,现有的方法可能缺乏稳定性.

研究的目的:

  • 提出一种基于设定值的概率的新概念.
  • 为不确定性下的统计估计提供统一和多功能框架.
  • 为了获得最佳性,凸度和稳定性条件以提高强度.

主要方法:

  • 使用定值概率的框架.
  • 使用定值概率的标量化技术.
  • 导出最佳条件并建立通用的凸度和稳定性.

主要成果:

  • 一个新的,统一的概率估计强度概念.
  • 优化,通用凸度和稳定性条件来自标量化.
  • 在金融投资组合管理和风险测量理论中证明了适用性.

结论:

  • 拟议的定值概率框架为统计估计提供了一个强大的方法.
  • 衍生条件在不确定的环境中提高了概率模型的可靠性.
  • 这一框架为优化决策和确保金融和风险管理的弹性提供了强有力的工具.