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相关概念视频

Bootstrapping01:24

Bootstrapping

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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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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Contingency Table01:29

Contingency Table

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A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
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There are only two possible outcomes,...
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Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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相关实验视频

Updated: May 10, 2025

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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基于贝叶斯快速双启动链的几何分布数据的控制图.

Muhammad Yahya Matdoan1,2, Muhammad Mashuri1, Muhammad Ahsan1

  • 1Department of Statistics, Faculty of Science and Data Analytics, Institut Teknologi Sepuluh Nopember, Kampus ITS-Sukolilo, Surabaya 60111, Indonesia.

MethodsX
|April 23, 2025
PubMed
概括
此摘要是机器生成的。

一种新的贝叶斯快速双引导 (BFDB) 方法改善了g图的参数估计,特别是在小样本大小的情况下. 与传统方法相比,BFDB为流程监控提供了更高的灵敏度和效率.

关键词:
贝叶斯的快速双启动链.最小偏差无偏差的最小偏差使用MVU估计器和BDFB估计器的g图.

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科学领域:

  • 工业工程 工业工程 工业工程
  • 统计过程控制 统计过程控制
  • 质量保证 质量保证 质量保证

背景情况:

  • 准确的参数估计对于使用g图表进行有效的过程控制至关重要.
  • 像最大概率和贝叶斯估计这样的传统方法在小样本大小的情况下可能不准确.
  • 最小方差无偏差 (MVU) 估计器和基于引导的贝叶斯估计器在检测大型过程转移方面存在局限性.

研究的目的:

  • 引入一种新的贝叶斯快速双引导 (BFDB) 方法,用于g图中的参数估计.
  • 提高过程监测的准确性和可靠性,特别是在小样本规模的场景中.
  • 为了提高显著工艺转移的检测.

主要方法:

  • 在g图表中开发贝叶斯快速双引导 (BFDB) 算法用于参数估计.
  • 对BFDB与现有方法进行比较分析,包括最小方差无偏差 (MVU) 估计器.
  • 在高质量的过程监控场景中评估灵敏度和计算效率.

主要成果:

  • 与MVU估计器相比,BFDB方法显示出更高的灵敏度和计算效率.
  • 在高质量的流程监控场景中,BFDB的表现始终优于MVU.
  • 该方法有效地处理小样本大小,并检测大工艺转移.

结论:

  • 拟议的BFDB方法为g图表的参数估计提供了显著的进步.
  • BFDB提供了一种更准确,更可靠的流程监控方法,特别是在具有挑战性的数据条件下.
  • 预计这一进步将改善工业过程控制和质量保证.