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相关概念视频

Poisson Probability Distribution01:09

Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

438
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
438
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...
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. 
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Binomial Probability Distribution01:15

Binomial Probability Distribution

10.8K
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:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
10.8K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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相关实验视频

Updated: Jul 5, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

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贝叶斯预测局限的Poisson分布式时间序列的贝叶斯预测

Feng-Chi Liu1, Cathy W S Chen1, Cheng-Ying Ho1

  • 1Department of Statistics, Feng Chia University, Taichung 40724, Taiwan.

Entropy (Basel, Switzerland)
|January 22, 2024
PubMed
概括

这项研究引入了贝叶斯的零-一-膨胀边界波桑自动回归 (ZOBPAR) 模型,用于时间序列数据. ZOBPAR模型准确地预测了包括空气质量指数水平在内的边界顺序数据.

科学领域:

  • 统计 统计 统计 统计
  • 时间序列分析时间序列分析
  • 计量经济学 计量经济学

背景情况:

  • 在空气质量指数 (AQI),经济学和信用评级中常见的普通时间序列数据通常与集中在特定状态 (例如0和1) 的概率相结合.
  • 现有的模型可能无法充分捕捉这些零-一-膨胀边界数据集的独特特征.

研究的目的:

  • 开发和验证贝叶斯式零-一-膨胀边界波桑自动回归 (ZOBPAR) 模型,用于建模和预测边界顺序时间序列.
  • 扩展ZOBPAR模型以纳入外源变量,以提高预测能力.

主要方法:

  • 为ZOBPAR模型提出贝叶斯推理方法.
  • 将外源变量纳入ZOBPAR框架.
  • 模拟研究用于评估参数估计的准确性和稳定性.

主要成果:

  • 模拟结果证实了准确的参数估计,随着样本大小的增加,后面的平均值趋于真值.
  • 对台湾每日AQI水平的实证应用表明,拟议的方法具有有效的预测能力.
  • ZOBPAR模型在预测Miaoli站的AQI方面表现特别强.

结论:

关键词:
空气质量指数是指空气质量指数.有界的鱼子分布值为整数的GARCH模型平行时间序列数据数据.零-一-膨胀的零-一个.

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  • 提出的贝叶斯ZOBPAR模型提供了一个强大的和准确的方法来建模和预测有界的顺序时间序列数据.
  • 包含外源变量代表了贝叶斯推理对这些类型的模型的重大进步.
  • 该方法的实际实用性通过成功的AQI预测来证明,突出其在环境监测和其他领域的潜力.