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

Random Error01:04

Random Error

848
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
848
Random and Systematic Errors01:20

Random and Systematic Errors

10.9K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
10.9K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

64
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
64
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

5.7K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
5.7K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

32
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
32
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

421
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...
421

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

Multi-mode ultrasonic-driven self-assembly of banana-derived resistant starch nanoparticles for epigallocatechin-gallate encapsulation and in-vitro antiglycation evaluation.

International journal of biological macromolecules·2026
Same author

Organizational factors influencing electronic health record adoption in Philippine public hospitals: A systematic review.

International journal of medical informatics·2026
Same author

Synchronous Surgical Therapy for Bilateral Multiple Pulmonary Nodules: A Single-Center Analysis of 108 Cases.

Thoracic cancer·2026
Same author

Efficacy of once-weekly teriparatide versus alendronate in Chinese postmenopausal osteoporosis: a randomised, open-label, active-controlled, 48-week, multicentre phase III study.

Journal of orthopaedic translation·2026
Same author

Comment on "Predictive value of high-density lipoprotein cholesterol and the cardio-ankle vascular index on cardiovascular outcomes in subjects with cardiovascular risks: the COUPLING Study".

Hypertension research : official journal of the Japanese Society of Hypertension·2026
Same author

Mapping burdens and inequalities of polycystic ovary syndrome in young females across 953 locations 1990-2040 with deep learning forecasts.

iScience·2026
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
查看所有相关文章

相关实验视频

Updated: Jun 12, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

图形模型推理与Erosely测量的数据.

Lili Zheng1, Genevera I Allen1,2,3,4,5

  • 1Department of Electrical and Computer Engineering, Rice University.

Journal of the American Statistical Association
|September 27, 2024
PubMed
概括
此摘要是机器生成的。

我们介绍GI-JOE,这是一种用于与不规则测量数据的高斯图形模型推理的新方法. 这种方法可以考虑不同的样本大小,提高复杂数据集中的图形选择精度.

关键词:
在FDR控制系统中,FDR控制器不均的测量 不均的测量图表选择 图表选择图形结构推断推断的结论缺失的数据 缺失的数据

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
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.9K

相关实验视频

Last Updated: Jun 12, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
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
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.9K

科学领域:

  • 统计 统计 统计 统计
  • 计算生物学 计算生物学
  • 神经科学是一个神经科学.

背景情况:

  • 高斯图形模型对于理解复杂系统至关重要.
  • 现实世界的数据经常显示不规则的测量 (侵蚀数据),导致跨节点对的样本大小变化.
  • 现有的方法无法解决图形推理中侵蚀测量所带来的挑战.

研究的目的:

  • 开发第一个专门为高斯图形模型设计的推理方法,使用侵蚀测量.
  • 提出一种方法,可以准确地描述由于数据不规则而导致的各个图边的不同不确定性水平.
  • 为了提供一个统计学上有效的方法,在有侵蚀数据的情况下进行图表选择.

主要方法:

  • 引入了GI-JOE (当联合观察被删除时的图形推理),一种边缘智能的推理方法.
  • 开发了一个相关的虚假发现率 (FDR) 控制程序.
  • 该方法对每个边缘的方差计算是根据其邻近节点的样本大小量身定制的.

主要成果:

  • 在侵蚀测量条件下,GI-JOE方法和FDR控制的统计有效性被证明.
  • 该方法成功地解释了各个图边的不同不确定性水平.
  • 与现有方法相比,模拟和真实神经科学数据集在图表选择方面表现出优异的性能.

结论:

  • GI-JOE提供了一个强大的解决方案,用于高斯图形模型推断与不规则测量数据.
  • 该方法能够处理不同的样本大小,在基因组学和神经科学等领域提供了显著的优势.
  • 这项工作填补了复杂的,现实世界的数据集的统计推理中的关键差距.