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相关概念视频

Prediction Intervals01:03

Prediction Intervals

2.2K
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. 
2.2K
Confidence Intervals01:21

Confidence Intervals

6.2K
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...
6.2K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

5.7K
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...
5.7K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

3.1K
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...
3.1K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.2K
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...
7.2K
Confidence Coefficient01:24

Confidence Coefficient

7.6K
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...
7.6K

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相关实验视频

Updated: Jun 13, 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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对两个参数负二项式分布的置信区间和预测区间.

Md Mahadi Hasan1, K Krishnamoorthy1

  • 1Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, USA.

Journal of applied statistics
|September 13, 2024
PubMed
概括

本研究为两参数负二项式分布引入了简单的置信区间 (CI) 和预测区间 (PI). 与现有的概率CI相比,这些新方法为中等样本大小提供了更高的准确性.

科学领域:

  • 统计 统计 统计 统计
  • 可能性理论概率理论.
  • 统计分布的统计分布

背景情况:

  • 两参数负二项式分布经常用于各种领域,包括生物学和质量控制.
  • 准确的置信区间 (CI) 和预测区间 (PI) 对于使用这种分布进行可靠的统计推理至关重要.
  • 构建CI和PI的现有方法可能是计算密集的或对中等样本大小的准确性较低.

研究的目的:

  • 开发和评估一个简单,准确的置信区间 (CI) 两个参数负二项式分布的平均值.
  • 建议和评估预测区间 (PI) 的性能,以此分布的未来样本的平均值.
  • 将拟议的方法与现有的基于概率的方法进行比较.

主要方法:

  • 开发基于大样本的方法来构建平均值的CI.
  • 拟议的信贷机构与传统的基于可能性的信贷机构的比较.
  • 为未来的样本平均值制定和评估预测间隔 (PI).
  • 将方法应用于现实世界的数据集,以实践示例.

主要成果:

  • 拟议的CI在计算上比概率CI更简单.
  • 新的信贷机构在适度样本大小方面表现优于概率信贷机构.
关键词:
采用联合抽样方法的方法.最大的概率估计估计.过度分散的情况.鱼类分布 鱼类分布预测间隔的时间评分方法 评分方法

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Last Updated: Jun 13, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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  • 提出的预测间隔为未来的观测提供了可靠的准确性.
  • 有关示例证实了开发方法的实际实用性和有效性.

    • 结论:

    • 建议的简单置信区间为现有的两参数负二项式分布方法提供了一个实用且准确的替代方案.
    • 开发的预测间隔对于预测未来的样本平均值是有效的.
    • 这些方法提高了负二项式分布后数据的统计分析能力.