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

Multiple Regression01:25

Multiple Regression

3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

571
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
571
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.0K
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...
1.0K
Binomial Probability Distribution01:15

Binomial Probability Distribution

15.2K
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,...
15.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

282
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...
282
Regression Toward the Mean01:52

Regression Toward the Mean

6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K

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

Evaluating the effects of glutaraldehyde concentration and incubation time on the structural integrity of the human pericardium.

Frontiers in bioengineering and biotechnology·2026
Same author

Markers of immune function and survival among cancer patients with HIV in the United States: a population-based registry linkage study.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

Solvent-dependent fluorescence dynamics and ultrafast optical nonlinearity in Tectona grandis L.f. leaf extract.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Incorporating Auxiliary Information into Assessment of Accuracy and Discrimination of Risk Models When Some Predictors are Missing.

Statistics in medicine·2026
Same author

Validation of a colorectal cancer risk prediction model in US Black women.

Cancer prevention research (Philadelphia, Pa.)·2026
Same author

Associations of local breast density with patient characteristics and risk of breast cancer among women undergoing diagnostic breast biopsy.

Breast cancer research : BCR·2026

相关实验视频

Updated: Jan 14, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K

一个强大的惩罚多项逻辑回归方法.

Cornelia Fuetterer1, Malte Nalenz2, Thomas Augustin2

  • 1Institute of AI and Informatics in Medicine, School of Medicine and Health, TUM University Hospital, Technical University of Munich (TUM), Ismaninger Straße 22, 81675 Munich, Germany.

Computational statistics
|October 27, 2025
PubMed
概括

我们引入了歧视力拉索 (DP-lasso),这是一种针对分类结果的新型惩罚回归方法. 在高维环境中,DP-lasso有效地选择了重要的预测因素,在模拟中表现优于现有的方法.

关键词:
集群集成是指集群集成.受到惩罚的回归.罚球权重是惩罚的权重.多种物流回归的多种物流回归.收缩 收缩 收缩 收缩单细胞RNA测序数据的数据变量选择 变量选择

更多相关视频

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

2.5K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

相关实验视频

Last Updated: Jan 14, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.7K
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

2.5K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 生物信息学是一种生物信息学.

背景情况:

  • 处罚回归方法对于高维数据中的预测和变量选择至关重要.
  • 现有的方法可能会与相关预测因子和复杂的分类结果作斗争.
  • 需要强大的规范化技术,平衡预测准确性和可解释性.

研究的目的:

  • 为多项物流模型提出一种新的惩罚性回归方法,即歧视性功率拉索 (DP-lasso).
  • 根据结果类别内和结果类别之间的距离,纳入预测因子特定的权重.
  • 评估DP-lasso在各种模拟环境中的性能与现有方法对比.

主要方法:

  • 开发了具有自适应L1类型惩罚期限的DP-lasso.
  • 提出了三种权重计算措施:基于ANOVA的和两个集群指数.
  • 通过不同数量的类别,预测因素,关联强度和相关性进行模拟.

主要成果:

  • 使用基于ANOVA的权重 (DPan) 的DP-lasso产生了较少的模型,特别是在高维设置中的相关预测器.
  • 当预测因素的数量 (p) 超过样本大小 (N) 时,DPan表现出优异的真实阳性率和低的虚假阳性率.
  • 在所有模拟场景中,DPan始终实现了高的真正阳性率和最低的假阳性率,包括当p < N时.

结论:

  • DPan是一种强烈推的方法,用于分析具有高维度预测器的分类结果.
  • 该方法有效地处理相关预测因素,并提高变量选择准确性.
  • 在使用单细胞RNA测序数据的超高维环境中证明了实用性.