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相关概念视频

Variation01:19

Variation

6.7K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
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Multiple Regression01:25

Multiple Regression

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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...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Biostatistics: Overview01:20

Biostatistics: Overview

220
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
220
Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
126
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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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...
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相关实验视频

Updated: Jun 5, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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在高维回归中选择变量的简单信息标准.

Matthieu Pluntz1, Cyril Dalmasso2, Pascale Tubert-Bitter1

  • 1High-Dimensional Biostatistics for Drug Safety and Genomics, CESP, Université Paris-Saclay, UVSQ, Université Paris-Sud, Inserm, Villejuif, France.

Statistics in medicine
|December 12, 2024
PubMed
概括
此摘要是机器生成的。

我们介绍了扩展的AIC (EAIC),这是一个用于高维回归中稀疏模型选择的新标准. 与AIC和BIC不同,EAIC控制虚假阳性率,提高了变量选择的准确性.

关键词:
在FWER中控制控制.拉索·拉索 (Lasso) 是一个高维回归的高维回归方法信息 信息标准 信息标准药物监督和药物监督选择变量的选择变量.

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相关实验视频

Last Updated: Jun 5, 2025

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
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Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

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科学领域:

  • 统计 统计 统计 统计
  • 生物信息学是一种生物信息学.
  • 药物监督 药物监督 药物监督

背景情况:

  • 在基因组学和药物研究中常见的高维回归需要选择稀疏的回归器.
  • 由于在变量选择中未解决多重测试,AIC和BIC等现有标准过于自由.

研究的目的:

  • 提出一个新的信息标准,扩展AIC (EAIC),用于在高维回归中进行强大的稀疏模型选择.
  • 为了确保在变量选择中进行非对称的家庭智能错误率 (FWER) 控制.

主要方法:

  • 开发了扩展的AIC (EAIC) 公式,包括日志概率,模型大小,总候选回归器和FWER目标.
  • 在线和物流回归设置中的模拟中使用LASSO对AIC,BIC,mBIC,mAIC和EBIC进行EAIC的评估.
  • 应用EAIC检测药物监督数据中的不良药物反应信号.

主要成果:

  • 在各种回归设置中,EAIC证明了有效的FWER控制,与AIC和BIC不同,AIC和BIC显示了许多虚假阳性.
  • 与其他标准相比,模拟研究证实了EAIC的优越变量选择性能.
  • 该方法证明有效地从现实世界药监数据中识别潜在的不良药物反应.

结论:

  • 扩展的AIC (EAIC) 为高维回归中的稀疏模型选择提供了一个统计学上合理的方法.
  • EAIC提供了更高的准确性和对错误阳性结果的控制,这对于基因组分析和药物监管等应用至关重要.
  • 在复杂的数据集中,EAIC是自动信号检测和可靠的变量选择的宝贵工具.