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

11.6K
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...
11.6K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.2K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.7K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.7K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.4K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

113
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
113
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

327
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...
327

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

Manipulating On/Off Single-Molecule Magnet Behavior in a Dy(III)-Based Photochromic Complex.

Journal of the American Chemical Society·2020
Same author

Donor Brain Death Leads to a Worse Ischemia-Reperfusion Injury and Biliary Injury After Liver Transplantation in Rats.

Transplantation proceedings·2020
Same author

Thioredoxin-Interacting Protein (TXNIP) Regulates Parkin/PINK1-mediated Mitophagy in Dopaminergic Neurons Under High-glucose Conditions: Implications for Molecular Links Between Parkinson's Disease and Diabetes.

Neuroscience bulletin·2020
Same author

Enhanced Room-Temperature Phosphorescence of an Organic Ligand in 3D Hybrid Materials Assisted by Adjacent Halogen Atom.

Inorganic chemistry·2020
Same author

Genome- and Community-Level Interaction Insights into Carbon Utilization and Element Cycling Functions of <i>Hydrothermarchaeota</i> in Hydrothermal Sediment.

mSystems·2020
Same author

Is tailored therapy based on antibiotic susceptibility effective ? a multicenter, open-label, randomized trial.

Frontiers of medicine·2020

相关实验视频

Updated: Jun 21, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

测量-$\delta$δ:一种非参数的层次聚类算法.

Jinli Yao, Jie Pan, Yong Zeng

    IEEE transactions on pattern analysis and machine intelligence
    |March 3, 2025
    PubMed
    概括

    一个新的非参数集群算法,Gauging-delta,有效地处理各种集群形状. 它使用适应性合并功能来进行层次合并,在合成和现实数据上表现优于现有方法.

    科学领域:

    • 计算机科学 计算机科学
    • 机器学习 机器学习
    • 数据挖掘 数据挖掘

    背景情况:

    • 聚类是核心的无监督学习任务,对于数据探索和模式发现至关重要.
    • 由于数据的复杂性和未知的集群结构,开发多功能,非参数集群算法仍然是一个重大挑战.

    研究的目的:

    • 介绍Gauging-delta,这是一个针对不同集群形状而设计的新型非参数集群算法.
    • 解决处理复杂数据分布的现有算法的局限性.

    主要方法:

    • 采用从单个数据点开始的层次化合并策略.
    • 使用自适应的合并功能来动态评估基于感知统计和环境背景的集群融合.

    主要成果:

    • 测量-delta在105个合成数据集中准确识别分离的集群方面表现出卓越的性能.
    • 在真实世界数据集上的实验强调了算法的有效性和对特征选择和距离指标的敏感性.

    结论:

    • 测量三角形为集群提供了强大的非参数方法,擅长处理各种集群几何形状.
    • 该研究强调了特征工程和度量选择对于优化集群结果在实际应用中的重要性.

    更多相关视频

    Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil
    12:03

    Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

    Published on: September 1, 2020

    Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
    09:49

    Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

    Published on: September 25, 2021

    相关实验视频

    Last Updated: Jun 21, 2026

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil
    12:03

    Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

    Published on: September 1, 2020

    Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
    09:49

    Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

    Published on: September 25, 2021