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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

408
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...
408
Binomial Probability Distribution01:15

Binomial Probability Distribution

10.4K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
10.4K
Poisson Probability Distribution01:09

Poisson Probability Distribution

7.8K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
7.8K
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
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

456
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...
456
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

124
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
124

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

ESPLSM: An Efficient and Interpretable Mediation Analysis Framework Using Sparse Envelopes.

Statistics in medicine·2026
Same author

Group sequential designs for survival outcomes with adaptive randomization.

Statistical methods in medical research·2025
Same author

MARGO: Machine Learning-Assisted Adaptive Randomization for Group Sequential Trials Based on Overlap Weights.

Statistics in medicine·2025
Same author

Optimal two-stage group sequential designs based on Mann-Whitney-Wilcoxon test.

PloS one·2025
Same author

A Personalized Dose-Finding Algorithm Based on Adaptive Gaussian Process Regression.

Pharmaceutical statistics·2024
Same author

Extracellular Microenvironment Alterations in Ductal Carcinoma In Situ and Invasive Breast Cancer Pathologies by Multiplexed Spatial Proteomics.

International journal of molecular sciences·2024
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
查看所有相关文章

相关实验视频

Updated: Jun 22, 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

贝叶斯推理用于具有隐藏包裹的多变量探针模型.

Kwangmin Lee1, Yeonhee Park2

  • 1Department of Big Data Convergence, Chonnam National University, Gwangju 61186, South Korea.

Biometrics
|July 1, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于分析多变量二进制数据的探测包模型. 这种新模型提高了统计建模中二元响应变量的估计效率.

关键词:
贝叶斯的推理 贝叶斯的推理细胞系数据分析数据分析.封面模型的模型.多个标签的分类.多变量探针模型的多变量探针模型

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K

相关实验视频

Last Updated: Jun 22, 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 Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K

科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习

背景情况:

  • 响应信封模型有效估计连续变量的回归系数.
  • 现有的方法仅限于连续响应变量,不包括二进制结果.

研究的目的:

  • 为多变量二进制响应变量提出具有隐藏包裹的多变量探针模型 (探针包裹模型).
  • 将响应信封模型的效率提升扩展到二进制数据分析.

主要方法:

  • 通过结合潜在的变量关系,开发了探测器外模型.
  • 使用基本可识别性概念来解决模型可识别性.
  • 采用贝叶斯的方法进行参数估计.

主要成果:

  • 模拟研究表明,与标准的多变量探头模型相比,可能会提高效率.
  • 现实世界的数据分析证明了该模型在多标签分类任务中的实用性.

结论:

  • 探测信封模型有效地将响应信封方法扩展到多变量二进制数据.
  • 这种模型可以提高估计效率和在诸如多标签分类等领域的实际应用.