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相关概念视频

Sampling Plans01:23

Sampling Plans

187
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
187
Sampling Distribution01:12

Sampling Distribution

12.8K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
12.8K
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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...
11.9K
Stratified Sampling Method01:16

Stratified Sampling Method

12.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
12.0K
Sampling Theorem01:15

Sampling Theorem

347
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
347
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

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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对于最大代表性亚样本的歧视性机器学习.

Tony Hauptmann1, Sophie Fellenz2, Laksan Nathan2

  • 1Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany. thauptmann@uni-mainz.de.

Scientific reports
|November 28, 2023
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概括
此摘要是机器生成的。

两种新的机器学习方法,最大代表性亚样本 (MRS) 和软MRS,减少社会科学数据的偏差. 这些技术使用代表性数据来调整样本重量,提高研究准确性和下游任务.

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科学领域:

  • 社会科学 社会科学 社会科学
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 偏差的人口样本是社会科学研究的一个重大挑战.
  • 现有的偏差缓解方法可能无法完全解决复杂的采样问题.

研究的目的:

  • 引入两种新的积极未标记的学习方法,最大代表性亚样本 (MRS) 和软MRS,以减轻人口样本中的偏差.
  • 评估MRS和软MRS在纠正偏差数据集和改进下游分析任务方面的有效性.

主要方法:

  • 开发了两种机器学习方法,MRS和软MRS,利用来自代表性数据集的辅助信息.
  • 训练有素的分类器来确定样本重量,MRS反复删除实例,Soft-MRS调整样本重量.
  • 在有偏见的公众普查数据集上验证了方法,并将性能与现有技术进行了比较.

主要成果:

  • 无论是MRS还是软MRS,都在减少人工创建的有偏见的数据集中的偏见方面表现出有效性.
  • 由MRS和软MRS生成的样本重量最小化了下游分类任务中的差异,并提高了下游分类任务的性能.
  • 推MRS用于分类任务,而软MRS适用于依赖变量偏差至关重要的任务.

结论:

  • 拟议的MRS和软MRS方法提供了一种基于机器学习的多功能方法,用于减少社会科学研究中的偏见.
  • 这些方法为提高社会科学研究结果的可靠性和通用性提供了实际解决方案.
  • 这项研究强调了这些技术在现实场景中的适用性,例如分析弹性对投票行为的影响.