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相关概念视频

Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

3.5K
A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
3.5K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
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...
1.6K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
Frequency-dependent Selection01:21

Frequency-dependent Selection

22.0K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
22.0K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

240
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
240

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相关实验视频

Updated: Jul 11, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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用基于竞争的程序对变量选择进行本地错误发现率估计.

Xiaoya Sun1,2, Yan Fu1,2

  • 1CEMS, NCMIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

Statistics in medicine
|November 6, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了TDfdr,这是一种用于估计局部错误发现率 (fdr) 的新方法,而不需要p值或已知的零分布. TDfdr在生物医学应用的高维数据分析中提供了更高的发现能力.

关键词:
功能选择 功能选择这是仿制品.当地的错误发现率.多重假设测试多重假设测试目标诱目标诱选择变量的选择变量.

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相关实验视频

Last Updated: Jul 11, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella
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An R-Based Landscape Validation of a Competing Risk Model
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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.
  • 基因组学就是基因组学.

背景情况:

  • 多重假设测试对于高维数据分析至关重要.
  • 错误发现率 (FDR) 是一种常见的错误控制措施.
  • 局部错误发现率 (fdr) 提供了个人假设的信心,但通常需要p值或已知的零分布,这可能是不可靠的.

研究的目的:

  • 提出一种新的方法,TDfdr,用于估计本地错误发现率 (fdr).
  • 开发一种不依赖p值或已知的零分布的fdr估计方法.
  • 评估TDfdr在高维数据分析和生物医学应用中的表现.

主要方法:

  • 开发了TDfdr,一种使用基于竞争的程序进行fdr估计的新方法.
  • 该方法灵感来自淘汰过器,避免依赖p值或已知的零分布.
  • 通过广泛的模拟研究验证并应用于现实世界生物医学数据集.

主要成果:

  • TDfdr使用基于竞争的程序准确估计了当地虚假发现率 (fdr).
  • 该方法与现有流行的方法相比,显示出更高的发现能力.
  • 成功应用于识别COVID-19相关蛋白质和HIV-1药物耐药性突变.

结论:

  • TDfdr提供了一种强大可靠的方法来估计本地错误发现率 (fdr).
  • 当p值或零分布不可用或不可靠时,这种方法特别有价值.
  • 在生物医学研究中推进高维数据分析方面,TDfdr显示出显著的前景.