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

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

73.1K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
73.1K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

295
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
295
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

449
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
449
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

37
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
37
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

619
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
619
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

3.0K
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...
3.0K

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

Discrepancies between self-reported alcohol use and breathalyzer results and factors associated with blood alcohol concentration positivity among road traffic injury patients in Cameroon.

Addiction (Abingdon, England)·2026
Same author

Impact of the COVID-19 pandemic on reported malaria incidence in children under five years of age in Cameroon: an interrupted time series analysis with regional heterogeneity (2017-2022).

Malaria journal·2026
Same author

Epidemiological patterns of motorcycle-related injuries in Cameroon: A comparative analysis of motorcycle users and pedestrians.

PLOS global public health·2026
Same author

Epidemiology of HIV in Remote Equatorial Regions of Cameroon: High Prevalence in Older Adults and Regional Disparities.

Tropical medicine and infectious disease·2025
Same author

Community willingness to participate in prehospital injury care: A cross-sectional survey of injury-prone areas along the national 3 highway in Cameroon.

PloS one·2025
Same author

Uptake of Intermittent Preventive Treatment and Its Associated Factors Among Pregnant Women in Cameroon: A Cross-Sectional Study.

Cureus·2025

相关实验视频

Updated: May 21, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.8K

一个代矩阵不确定性选择器用于具有测量错误的高维通用线性模型.

Betrand Fesuh Nono1, Georges Nguefack-Tsague2, Martin Kegnenlezom3

  • 1National Advanced School of Engineering, University of Yaoundé I, Cameroon.

Statistical methods in medical research
|March 19, 2025
PubMed
概括

一种新方法,即代矩阵不确定性选择器 (IMUS),为具有测量误差的高维回归提供了有效的变量选择. IMUS是一种无错误分布的方法,在模拟和现实世界数据分析中表现良好.

关键词:
一般化的线性模型.基因表达的基因表达方式高维数据的高维数据.代重权最小平方.矩阵不确定性选择器测量时出现的测量误差

更多相关视频

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

2.4K
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

相关实验视频

Last Updated: May 21, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.8K
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

2.4K
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

科学领域:

  • 统计 统计 统计 统计
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 测量误差是高维通用线性回归的一个重大挑战.
  • 现有的规范化方法经常与测量误差作斗争,需要计算密集的误差分布估计.
  • 需要强大的,无错误分布的变量选择技术.

研究的目的:

  • 引入代矩阵不确定性选择器 (IMUS),这是一个新的无错误分布方法,用于在高维通用线性回归中进行变量选择.
  • 评估IMUS的性能与模拟和现实数据集中的现有方法相比.
  • 为解决回归分析中的测量误差提供一种高效可靠的工具.

主要方法:

  • 基于矩阵不确定性选择器框架开发了代矩阵不确定性选择器 (IMUS).
  • 实施了一种有效的代算法,适用于指数家族内的通用线性模型.
  • 通过逻辑和波桑回归的模拟以及在三个微阵列基因表达数据集上验证IMUS.

主要成果:

  • 与其他无错误分布方法相比,IMUS证明了有效的共同变量选择,具有更顺的收率和更清晰的肘部标准.
  • 模拟研究表明,IMUS在共同变量选择中与通用矩阵不确定性选择器 (GMUS) 和通用矩阵不确定性拉索 (GMUL) 的性能相当.
  • 与GMUS和GMUL相比,IMUS在微阵列数据集上表现出较小的估计误差和优越的收性质,这些数据集面临着收问题或缺乏明确的选择标准.

结论:

  • IMUS提供了一种强大而高效的无错误分布方法,用于在高维通用线性回归中进行变量选择,并具有测量错误.
  • 该方法具有实用优势,包括更顺的融合和明确的选择标准,使其适用于复杂的生物数据.
  • IMUS为克服统计建模中测量误差所带来的挑战提供了一个有前途的解决方案.