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相关概念视频

Confidence Intervals01:21

Confidence Intervals

6.5K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
6.5K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

6.0K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
6.0K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.1K
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Confidence Coefficient01:24

Confidence Coefficient

7.7K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.7K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.6K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.6K

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相关实验视频

Updated: Jul 19, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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来自交叉验证的考克斯模型测试错误的置信区间.

Min Woo Sun1, Robert Tibshirani1,2

  • 1Department of Biomedical Data Science, Stanford University, Stanford, California, USA.

Statistics in medicine
|August 15, 2023
PubMed
概括
此摘要是机器生成的。

标准交叉验证 (CV) 可以低估模型测试错误,因为相关的估计. 嵌套的CV改善了统计学习模型的置信区间覆盖率,包括Cox比例危险模型.

关键词:
考克斯模型 考克斯模型信心区间的时间间隔是信任区间.进行交叉验证.嵌套的交叉验证.生存分析,生存分析.

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相关实验视频

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Assessment and Communication for People with Disorders of Consciousness
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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科学领域:

  • 统计学学习 统计学学习
  • 机器学习模型评估评估
  • 对生存分析的分析.

背景情况:

  • 交叉验证 (CV) 是估计模型测试误差的标准方法.
  • 由于相关的错误估计,标准CV可能会产生不充分覆盖的置信区间.
  • 这种低估是因为数据样本用于培训和测试.

研究的目的:

  • 为解决模型评估标准交叉验证的覆盖范围问题.
  • 将嵌套交叉验证方法推广为改进的差异估计.
  • 探索使用嵌套的CV.Cox比例危险模型的测试误差估计.

主要方法:

  • 实施嵌套交叉验证 (嵌套CV) 以估计预测错误.
  • 计算预测错误的平均平方误差,以计算相关性.
  • 将嵌套CV应用到Cox比例危险模型框架中.

主要成果:

  • 与标准CV相比,嵌套CV显示出更高的置信区间覆盖率.
  • 拟议的方法减轻了测试误差估计差异低估的情况.
  • 该研究探讨了考克斯模型背景下的各种测试错误指标.

结论:

  • 嵌套CV提供了一种更可靠的方法来评估模型性能和不确定性.
  • 这种技术在生存分析中的Cox比例危险模型中尤为有价值.
  • 在CV估计中考虑相关性对于准确的模型评估至关重要.