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相关概念视频

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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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...
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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.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Survival Curves01:18

Survival Curves

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Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
623
Poisson Probability Distribution01:09

Poisson Probability Distribution

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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...
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Poisson's And Laplace's Equation01:25

Poisson's And Laplace's Equation

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The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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相关实验视频

Updated: Jan 9, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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使用非对称拉普拉斯分布的学习生存分布.

Deming Sheng1, Ricardo Henao1

  • 1Duke University.

Proceedings of machine learning research
|December 4, 2025
PubMed
概括

这项研究引入了一种使用非对称拉普拉斯分布 (ALD) 的新型参数生存分析方法. 与现有的参数和非参数方法相比,ALD模型提供了更高的精度,区分和校准,用于事件时间估计.

科学领域:

  • 统计 统计 统计 统计
  • 生物统计学 生物统计学
  • 机器学习 机器学习

背景情况:

  • 概率生存分析模型使用共变量估计事件发生时间.
  • 非参数方法越来越受青,估计概率或量值,而不是直接分布.
  • 监督学习通常用于现代生存分析.

研究的目的:

  • 提出一种新的参数生存分析方法.
  • 利用非对称拉普拉斯分布 (ALD) 来改进生存模型.
  • 为了使关键事件总结能够以封闭形式计算.

主要方法:

  • 开发了一个基于非对称拉普拉斯分布 (ALD) 的参数生存模型.
  • 使用最大概率估计来优化个人层面的ALD参数 (位置,规模,不对称性).
  • 将拟议的方法与现有的参数和非参数方法进行比较.

主要成果:

  • 基于ALD的模型允许对平均值,中位数,模式,变化和量值进行封闭式计算.
  • 进行了广泛的模拟和现实世界的数据分析.
  • 拟议的方法在准确性,区分和校准方面表现出卓越的性能.

结论:

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  • 使用ALD提出的参数生存分析方法提供了显著的优势.
  • 这种方法优于传统的参数和非参数生存模型.
  • 该方法提供了对事件时间总结的准确和精确校准的估计.