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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

57
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
57
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

85
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
85
Linear time-invariant Systems01:23

Linear time-invariant Systems

264
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
264
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

450
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...
450
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

531
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
531
Random Error01:04

Random Error

899
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
899

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相关实验视频

Updated: Jul 12, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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用于单模时间序列预测的高斯式和勒赫式模型.

Azzouz Dermoune1, Daoud Ounaissi2, Yousri Slaoui3

  • 1CNRS, Laboratoire Paul Painlev, UMR 8524, Université de Lille, 59653 Villeneuve d'ascq, France.

Entropy (Basel, Switzerland)
|October 28, 2023
PubMed
概括

这项研究引入了高斯和莱尔赫模型用于单模时间序列预测,比较使用COVID-19数据的参数估计方法. 这项研究为每日感染预测提供了置信区间.

关键词:
高斯的模型是高斯的模型.莱尔赫模型的模型.尼尔德米德的意思是什么每天感染的感染.最小绝对偏差的最小绝对偏差最好的功能是最优的功能.简单的算法简单的算法.

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科学领域:

  • 统计 统计 统计 统计
  • 时间序列分析时间序列分析
  • 流行病学建模 流行病学建模

背景情况:

  • 单模时间序列预测带来了独特的挑战.
  • 准确的传染病传播模型,如COVID-19,对于公共卫生至关重要.
  • 现有的预测模型可能无法完全捕捉单模数据的特征.

研究的目的:

  • 提出和评估高斯和勒赫模型用于单模时间序列预测.
  • 为了比较参数估计技术,特别是与加权中位数和没有加权中位数的绝对余量最小化.
  • 应用这些模型来预测中国的每日COVID-19感染情况,并得出置信区间.

主要方法:

  • 开发高斯 (3参数) 和莱尔赫 (4参数) 模型.
  • 通过最小化绝对残余的总和来估计参数.
  • 估计方法的比较:有和没有加权中位数.
  • 应用于来自中国的每日COVID-19感染数据.

主要成果:

  • 斯模型和莱尔赫模型都应用于COVID-19每日感染数据.
  • 用两个不同的最小化方法进行了参数估计.
  • 每日感染预测的置信区间成功地从局部最小值推导出来.
  • 进行了加权中位数方法与没有加权中位数方法的性能比较.

结论:

  • 高斯和莱尔赫模型为单模时间序列预测提供了可行的方法.
  • 选择参数估计方法 (有或没有加权中位数) 影响模型结果.
  • 这些模型提供了一个框架,用于在流行病学预测中生成置信区间.
  • 这项研究证明了这些模型在有关COVID-19传播的现实应用中的实用性.