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相关概念视频

Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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...
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Student t Distribution01:31

Student t Distribution

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The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...
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Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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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...
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Sampling Distribution01:12

Sampling Distribution

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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...
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The Anderson-Darling Test01:16

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The Anderson-Darling test is a statistical method used to determine whether a data sample is likely drawn from a specific theoretical distribution. Unlike parametric tests, it does not require assumptions about specific parameters of the distribution. Instead, it compares the sample's empirical cumulative distribution function (ECDF) with the cumulative distribution function (CDF) of the hypothesized distribution. Critical values for the test are specific to the chosen distribution rather...
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相关实验视频

Updated: Jan 11, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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适应性样本选择用于在分布转移下通过最小规范化协差决定因素进行个体试样预测.

Xudong Huang1, Xiaojing Chen2, Yong He3

  • 1School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun, 130022, Jilin, China.

Analytical and bioanalytical chemistry
|November 18, 2025
PubMed
概括

分布转移阻碍了模型的准确性. 适应性最小调整共变量决定子 (AMRCD) 方法通过选择相关的训练数据来改进部分最小平方 (PLS),提高高维问题的预测性能.

关键词:
分布转移转移是分布转移的原因之一.高维度的高维度的高维度.最小的共差决定因素.部分最小正方形.样本的选择 样本的选择

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科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 当训练和现实数据不同时,分布转移会显著降低模型性能.
  • 对于高维数据来说常见的部分最小平方 (PLS) 回归易受分布转移的影响.
  • 现有的方法很难将PLS模型适应不断变化的数据分布.

研究的目的:

  • 引入一种新的方法来提高在分布转移下PLS模型的准确性.
  • 通过自适应地选择培训样本来提高PLS模型的概括能力.
  • 在高维设置中确保协变矩阵的数值稳定性.

主要方法:

  • 开发了自适应性最小规范化协差决定子 (AMRCD) 方法.
  • AMRCD通过适应性选择与单一测试样本分布相匹配的训练样本.
  • 整合了规范化技术,以保持良好的条件共变矩阵.

主要成果:

  • 该AMRCD方法显著提高了测试样本的预测准确性.
  • 超越了经典的PLS和一个替代的样本选择框架.
  • 在三个模拟和两个现实数据集上进行了验证.

结论:

  • AMRCD有效地解决了PLS回归中的分布转移挑战.
  • 该方法提高了预测准确性和模型概括性.
  • AMRCD为具有不同分布的高维数据分析提供了强大的解决方案.