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

394
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...
394
Quartile01:15

Quartile

4.2K
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
4.2K
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
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

184
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...
184
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
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...
1.5K
Modified Boxplots00:57

Modified Boxplots

9.3K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
9.3K

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

A Chromosome-Level Genome Assembly of Sitotroga cerealella (Olivier, 1789) (Lepidoptera: Gelechiidae), a Global Pest of Stored Grains.

Scientific data·2026
Same author

Impact of Harvest Timing and Stir-Frying on the Bioactive Compounds, Bioactivities, and Flavor of <i>Ziziphi Spinosae Semen</i>: An Integrated Analysis via GC-IMS, Electronic Sensors, and <i>Caenorhabditis elegans</i> Model.

Plants (Basel, Switzerland)·2026
Same author

Natural versus GnRHa-HRT cycle for FET in tubal infertility with prior failed natural cycles: a propensity score-matched retrospective cohort study.

European journal of obstetrics, gynecology, and reproductive biology·2026
Same author

Calumenin prevents fibroblast senescence and lung aging by promoting vimentin proteostasis.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Artificial intelligence empowers gut microbiota research in neurodegenerative diseases molecular mechanisms and precision therapy.

iScience·2025
Same author

Macrophages and Tissue Homeostasis: From Physiological Functions to Disease Onset.

Frontiers in bioscience (Landmark edition)·2025

相关实验视频

Updated: Jun 18, 2025

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

10.1K

功能性定量回归的极端条件定量的估计.

Hanbing Zhu1, Riquan Zhang1, Yehua Li2

  • 1East China Normal University.

Statistica Sinica
|July 30, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于估计极端条件量子的新方法,提高了重尾数据量子回归的稳定性. 新型的功能复合定量回归增强了对响应变量尾巴的分析.

关键词:
额外推算是指进行额外推算.极端的量化,极端的量化.极端价值理论是一个极端价值理论.功能性主要组件分析分析函数定量回归的功能回归.重尾分布式分布的重尾分布式分布

更多相关视频

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.1K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.3K

相关实验视频

Last Updated: Jun 18, 2025

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

10.1K
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.1K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.3K

科学领域:

  • 统计 统计 统计 统计
  • 计量经济学 计量经济学
  • 功能数据分析 功能数据分析

背景情况:

  • 量子位回归提供了响应-共变量关系的详细视图,特别是在极端的量子位.
  • 传统方法由于数据稀疏性和重尾分布,在极端尾中扎不稳定.

研究的目的:

  • 开发一种新型,稳定的极端条件量数的估计器.
  • 为了解决稀疏,重尾数据场景中常规量子位回归的局限性.

主要方法:

  • 功能复合量子位回归包括功能主要组件分析.
  • 从极端价值理论中应用一种推断技术,用于增强尾部估计.
  • 在规律性条件下建立的非对称的正常性.

主要成果:

  • 建议的估计器证明了极端条件定量值的稳定性和准确性得到了改善.
  • 蒙特卡洛模拟证实了与现有估计方法相比,性能优越.
  • 在两个真实数据集上的经验分析验证了新方法的实际实用性.

结论:

  • 新的功能复合量子位回归方法为分析极端量子位提供了强大的方法.
  • 这种技术对于重尾分布和稀疏数据特别有价值,提供了一个全面的统计工具.