Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Censoring Survival Data01:09

Censoring Survival Data

96
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
96
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

438
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...
438
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

146
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
146
Probability Distributions01:32

Probability Distributions

7.0K
 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...
7.0K
Uniform Distribution01:19

Uniform Distribution

5.0K
The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
5.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Statistical analysis of disease onset during lifespan with left truncation.

Biometrics·2026
Same author

A fiber-reinforced bioresponsive hierarchical tampon-inspired dressing orchestrates synergistic hemostasis and controlled antioxidant delivery for female reproductive tract repair.

International journal of biological macromolecules·2026
Same author

Regression for Left-Truncated and Right-Censored Data: A Semiparametric Sieve Likelihood Approach.

Statistics in medicine·2026
Same author

Nonparametric estimation of conditional survival function with time-varying covariates using DeepONet.

Lifetime data analysis·2026
Same author

Pulmonary artery sarcoma with mediastinal metastasis: a case report.

Frontiers in oncology·2026
Same author

Clinical Manifestations.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same journal

Classification Under Local Differential Privacy with Model Reversal and Model Averaging.

Journal of machine learning research : JMLR·2026
Same journal

Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data.

Journal of machine learning research : JMLR·2026
Same journal

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.

Journal of machine learning research : JMLR·2026
Same journal

Unsupervised Tree Boosting for Learning Probability Distributions.

Journal of machine learning research : JMLR·2026
Same journal

A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations.

Journal of machine learning research : JMLR·2026
Same journal

Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes.

Journal of machine learning research : JMLR·2026
查看所有相关文章

相关实验视频

Updated: Jul 5, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

使用神经网络对受审查和不受审查的数据进行条件分布函数估计.

Bingqing Hu1, Bin Nan1

  • 1Department of Statistics University of California, Irvine Irvine, CA 92697, USA.

Journal of machine learning research : JMLR
|January 22, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的神经网络方法,用于用受审查的数据估计条件分布函数. 该方法提供了准确的,没有假设的预测,在模型假设未满足时,其表现优于传统技术.

关键词:
条件分布估计估计条件分布估计神经网络的神经网络的神经网络预测区间的预测区间生存分析,生存分析.时间变化的共变量.

更多相关视频

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.1K

相关实验视频

Last Updated: Jul 5, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.1K

科学领域:

  • 机器学习 机器学习
  • 生存分析的分析.
  • 统计建模 统计建模

背景情况:

  • 传统的神经网络经常估计有条件的平均值,限制了它们在生存分析中的应用.
  • 对于被审查数据的现有方法可能依赖于限制性模型假设,从而导致偏见的结果.

研究的目的:

  • 开发一种神经网络方法,用受审查和未受审查的数据来估计条件分布函数.
  • 提供一种无假设的方法,有效地处理时间依赖的协变量.

主要方法:

  • 拟议的算法使用了与考克斯回归和时间依赖共变量兼容的数据结构.
  • 使用基于全概率的损失函数,将条件危险函数视为非参数参数.
  • 无约束优化方法用于高效的参数估计.

主要成果:

  • 模拟研究表明,与部分概率和传统神经网络相比,拟议的方法的性能优越.
  • 新的方法产生了公正的估计,即使违反标准模型假设.
  • 该方法的有效性在现实世界数据集上得到进一步验证.

结论:

  • 新型神经网络方法准确地估计了对受审查和未受审查数据的条件分布函数,而无需强加模型假设.
  • 这种方法为现有方法提供了强大的替代方案,特别是在违反假设或时间依赖的共变量的情况下.