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

Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

139
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
139
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
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

587
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...
587
Inverse z-Transform by Partial Fraction Expansion01:20

Inverse z-Transform by Partial Fraction Expansion

375
The inverse z-transform is a crucial technique for converting a function from its z-domain representation back to the time domain. One effective method for finding the inverse z-transform is the Partial Fraction Method, which involves decomposing a function into simpler fractions with distinct coefficients. These fractions correspond to known z-transform pairs, facilitating the inverse transformation process.
To begin the process, the poles of the function are identified and the function is...
375

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

Predicting birth weight by multivariate functional principal component regressions.

The international journal of biostatistics·2026
Same author

Strain-Volume Loop Dynamics: A Novel Perspective on Left Ventricular Dysfunction.

Korean circulation journal·2026
Same author

Graph Frequency-Domain Factor Modeling.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Cross-Spectral Analysis of Bivariate Graph Signals.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Differential Responses to Cigarette Package Labeling Alternatives Among Adults Who Smoke: Results From a Randomized Trial.

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco·2024
Same author

Heated tobacco product use frequency, smoking quit attempts, and smoking reduction among Mexican adult smokers.

Tobacco induced diseases·2024

相关实验视频

Updated: Jul 25, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K

零膨胀时间序列集群通过集体厚笔转换.

Minji Kim1, Hee-Seok Oh2, Yaeji Lim3

  • 1Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, North Carolina, USA.

Journal of classification
|June 26, 2023
PubMed
概括

这项研究引入了一个集体厚笔转换 (e-TPT) 来聚类高维,零膨胀时间序列数据. 这种新的方法增强了时间分辨率,提高了诸如步数和COVID-19病例等数据集的聚类精度.

关键词:
集群集成是指集群集成.多个尺度的方法方法.新确认的COVID-19病例数据步数数据 步数数据 步数数据厚笔转换变形 厚笔转变变形零膨胀时间序列数据数据.

更多相关视频

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.2K
Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.1K

相关实验视频

Last Updated: Jul 25, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.5K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.2K
Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.1K

科学领域:

  • 数据科学数据科学数据科学
  • 时间序列分析时间序列分析
  • 统计建模 统计建模

背景情况:

  • 聚类高维时间序列数据,特别是在零通货膨胀的情况下,存在重大挑战.
  • 现有的方法经常与这些数据固有的时间依赖性和稀疏性作斗争.
  • 有效的集群对于发现复杂数据集中的模式至关重要.

研究的目的:

  • 为高维零膨胀时间序列数据开发一种新的聚类方法.
  • 提高时间序列数据的时间分辨率,以改善聚类.
  • 引入一个强大的相似度测量和一个针对这种数据类型量身定制的高效聚类算法.

主要方法:

  • 开发一个集体厚笔转换 (e-TPT) 来提高时间分辨率.
  • 修改的相似度指标的定义,包括对零膨胀数据的e-TPT.
  • 提出了一种高效的代聚类算法,该算法针对新的相似度量进行了优化.

主要成果:

  • 提出的基于e-TPT的聚类方法在模拟实验中表现出卓越的性能.
  • 实现了现实世界数据集的有效集群,包括步数数据和每日COVID-19病例数据.
  • 该方法成功地解决了时间序列中高维度和零通货膨胀的挑战.

结论:

  • 集体厚笔转换 (e-TPT) 提供了一种强大的方法,用于集群零膨胀时间序列.
  • 开发的方法提供了增强的时间分辨率,这对于精确的时间序列聚类至关重要.
  • 这种技术在分析与健康和活动相关的时间序列数据方面具有实际应用.