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

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

14.1K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
14.1K
Censoring Survival Data01:09

Censoring Survival Data

518
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...
518
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

9.3K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
9.3K
Sign Test for Median of Single Population01:20

Sign Test for Median of Single Population

339
In general, the sign test serves as a nonparametric method to test hypotheses about the median of a single population when the data does not follow a known distribution. This simplicity makes it particularly useful for small sample sizes or when the assumptions of parametric tests cannot be met. The process begins with identifying a null hypothesis, typically stating that the population median equals a specific value. The alternative hypothesis could be that the median is either not equal to,...
339
Confidence Coefficient01:24

Confidence Coefficient

10.4K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
10.4K
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

430
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
430

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

Reframing AI for Rare Disease Recognition.

Research square·2026
Same author

Translating AI research into reality: summary of the 2025 voice AI Symposium and Hackathon.

Frontiers in digital health·2026
Same author

The need to develop health data transaction disclosure requirements to balance transparency, privacy, and progressive use.

The Lancet. Digital health·2026
Same author

Scale-up Unlearnable Examples Learning with High-Performance Computing.

IS&T International Symposium on Electronic Imaging·2025
Same author

Re-identification risk for common privacy preserving patient matching strategies when shared with de-identified demographics.

Journal of the American Medical Informatics Association : JAMIA·2025
Same author

Examining Online Caregiving Discussions Across Racial Groups of Informal Alzheimer's Disease Caregivers.

Studies in health technology and informatics·2025
Same journal

Zero-shot reconstruction of mutant spatial transcriptomes.

Patterns (New York, N.Y.)·2026
Same journal

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026
Same journal

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems.

Patterns (New York, N.Y.)·2026
Same journal

Spacing effect improves generalization in biological and artificial systems.

Patterns (New York, N.Y.)·2026
Same journal

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026
Same journal

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026
查看所有相关文章

相关实验视频

Updated: Jan 14, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

一个共识的隐私指标框架,用于合成数据.

Lisa Pilgram1,2,3, Fida Kamal Dankar2, Jörg Drechsler4,5,6

  • 1School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON K1H 8M5, Canada.

Patterns (New York, N.Y.)
|October 27, 2025
PubMed
概括
此摘要是机器生成的。

合成数据生成提供了一种分享敏感信息的方法,但必须证明隐私. 本研究提出了一个评估隐私风险的框架,建议不要使用相似度指标,并专注于成员身份和属性披露,以获得更好的保护.

关键词:
属性披露 属性披露分享数据的数据共享.生成型的人工智能 (GAI)披露身份的披露.成员身份披露 成员资格披露隐私 隐私 隐私 隐私 隐私 隐私综合数据 综合数据

相关实验视频

Last Updated: Jan 14, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

科学领域:

  • 计算机科学 计算机科学
  • 数据 隐私 数据 隐私 数据
  • 统计建模 统计建模

背景情况:

  • 合成数据生成对于敏感部门的二次数据使用至关重要.
  • 确保合成数据中的个人隐私对于道德和法律合规至关重要.
  • 现有的隐私评估指标往往缺乏明确的解释.

研究的目的:

  • 开发一个基于共识的框架来评估合成数据中的隐私.
  • 识别和推评估隐私风险的有效指标.
  • 引导负责任地采用合成数据生成.

主要方法:

  • 专家达成共识的过程,以制定隐私评估框架.
  • 分析常用的隐私指标及其局限性.
  • 确定关键的披露类型:成员资格和属性披露.

主要成果:

  • 通常使用的真实数据和合成数据之间的相似度指标缺乏准确的解释,应避免用于隐私评估.
  • 在评估会员资格和属性披露的重要性方面达成共识.
  • 拟议的框架为有效衡量这些披露提供了建议.

结论:

  • 为合成数据隐私评估建立了一个强大的框架,优先考虑会员资格和属性披露.
  • 该框架的建议适用于不同的私有合成数据.
  • 需要进一步的研究来支持广泛采用保护隐私的合成数据.