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相关概念视频

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. 
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Expected Value01:15

Expected Value

4.0K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
4.0K
Unusual Results01:16

Unusual Results

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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
3.2K
Random Variables01:09

Random Variables

12.4K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
12.4K
Probability Distributions01:32

Probability Distributions

7.3K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
7.3K
Probability in Statistics01:14

Probability in Statistics

13.5K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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在可交换序列中对极端值的贝叶斯预测.

Bruce M Hill1

  • 1Department of Statistics, University of Michigan, Ann Arbor, MI 48109.

Journal of research of the National Institute of Standards and Technology
|July 5, 2023
PubMed
概括

这项研究通过修改Hill tail指数估计器来增强长尾分布的极端值预测. 有限域模型为未来的记录值提供了更现实的预测.

科学领域:

  • 统计 统计 统计 统计
  • 可能性理论概率理论.

背景情况:

  • 在可交换的序列中预测极端和记录值带来了理论上的挑战.
  • 对于长尾分布的标准理想化模型可以在假设无边界数据时产生不切实际的预测.

研究的目的:

  • 开发用于预测极端和记录值的新理论和方法.
  • 为改进预测准确性而调整Hill尾巴指数估计器.
  • 为了实际预测,将理想化的模型与有限的可观察数据相协调.

主要方法:

  • 修改了Hill尾巴指数估计器用于预测.
  • 对长尾分布的有限与无限理想化模型的分析.
  • 后期预期在长尾背景下对预测的应用.

主要成果:

  • 修改后的希尔估计器为未来的变量提供了适当的预测.
  • 有限域建模从无限的理想化模型中解决了不现实的预测.
  • 显示下一个记录值的预测值在当前记录的几倍之内.

结论:

  • 有限域模型与长尾分布的合理预测方法兼容.
  • 后期预期适用于预测长尾分布,类似于其他科学背景.
关键词:
贝叶斯预测是贝叶斯的预测.交换能力可以交换.长尾分布 长尾分布记录价值的记录值是什么意思尾部指数估计器的估计器

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  • 开发的方法对于预测极端和记录值是有效的,模拟证明了这一点.