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

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

5.7K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
5.7K
Confidence Intervals01:21

Confidence Intervals

6.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
6.2K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.2K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.2K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

3.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
3.1K
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Confidence Coefficient01:24

Confidence Coefficient

7.6K
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...
7.6K

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

Multiple imputation confidence intervals for the mean of the discrete distributions for incomplete data.

Statistics in medicine·2021
Same author

Exploring the Association of Autism Spectrum Disorders and Constipation through Analysis of the Gut Microbiome.

International journal of environmental research and public health·2021
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
Same journal

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same journal

Rejoinder to Commentaries on "A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events".

Statistics in medicine·2026
Same journal

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Statistics in medicine·2026
Same journal

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Statistics in medicine·2026
查看所有相关文章

相关实验视频

Updated: Jun 13, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

多重推算信心区间对于一个缺失观察的风险差异.

Chung-Han Lee1

  • 1Department of Statistics, National Cheng Kung University, Tainan, Taiwan.

Statistics in medicine
|June 10, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了多重归算对差异估计回收方法 (MOVER) 以改善对不完整数据的风险差异的置信区间估计. 新方法提供了更准确的覆盖概率,特别是在参数边界附近.

关键词:
鱼类分布 鱼类分布二项式分布的二项式分布覆盖范围的概率 覆盖范围的概率不完整的数据不完全的数据.缺失的数据 缺失的数据失踪不是随机发生的.

更多相关视频

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K
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

10.1K

相关实验视频

Last Updated: Jun 13, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K
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

10.1K

科学领域:

  • 生物统计学 生物统计学
  • 统计推理 统计推理
  • 数据分析 数据分析

背景情况:

  • 对风险差异的置信区间在许多领域都至关重要.
  • 差异估计回收方法 (MOVER) 是它们估计的标准技术.
  • 在信任区间估计中处理不完整数据 (缺失值) 是一个重大挑战.

研究的目的:

  • 开发和评估MOVER的多种归算程序,以估计风险差异置信区间.
  • 为了解决随机丢失和不随机丢失的数据场景.
  • 为了提高信任区间覆盖概率的准确性.

主要方法:

  • 建议针对MOVER进行量身定制的新型多重归算技术.
  • 将这些方法应用于Poisson和二项式分布.
  • 进行模拟研究以比较现有方法的性能.

主要成果:

  • 拟议的多重归算MOVER间隔表明覆盖率更接近名义水平.
  • 当真实参数接近边界时,性能改进尤其显著.
  • 这些方法使用现实数据示例进行了验证.

结论:

  • 多重归算显著提高了风险差异置信区间估计与不完整数据的MOVER.
  • 提出的方法对于随机丢失和非随机丢失的数据都是可靠的.
  • 这种方法在缺少数据的情况下提供了更可靠的统计推理工具.