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相关概念视频

Determination of Expected Frequency01:08

Determination of Expected Frequency

2.1K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

38
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
38
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

40
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...
40
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

27
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
27
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

363
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...
363
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

93
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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相关实验视频

Updated: Jun 6, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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一个全面的多目标框架,用于估计碰撞频率模型的估计.

Zeke Ahern1, Paul Corry2, Mohammadali Shirazi3

  • 1School of Civil & Environment Engineering, Queensland University of Technology, 2 George Street, Brisbane, 4000 QLD, Australia.

Accident; analysis and prevention
|December 3, 2024
PubMed
概括

这项研究引入了一种新的崩数据分析框架,优化模型以处理未观察到的异质性并提高准确性. 该MetaCountRegressor Python包提供了一个系统的方法,用于更好的崩频率建模.

关键词:
相关的随机参数.崩频率 崩的频率假设测试 测试 假设测试这是一种元启发式 (metaheuristic) 启发式.优化优化 优化优化

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相关实验视频

Last Updated: Jun 6, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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科学领域:

  • 交通安全与工程
  • 统计建模 统计建模
  • 运输科学 运输科学

背景情况:

  • 未观察到的异质性是崩频率分析的一个重大挑战,通常用随机参数和专业分布来解决.
  • 现有的方法需要对功能形式,转换和贡献因素进行广泛的假设测试,风险偏差和次优模型.
  • 同时考虑多个目标,如适合性和各种异质性方面,使模型开发复杂化.

研究的目的:

  • 提出一个全面的优化框架,用于系统的假设测试在崩数据建模.
  • 为了应对未观察到的异质性的挑战,同时分组随机参数,功能形式和贡献因素识别.
  • 减少与碰撞频率分析中有限测试相关的偏差和成本.

主要方法:

  • 开发了一个数学编程公式,用于全面的优化框架.
  • 使用元启发式算法 (模拟化,差异进化,和搜索) 进行复杂的估计和优化.
  • 通过使用三个真实世界的崩数据集验证了框架.

主要成果:

  • 拟议的框架有效地估计了健全和节的撞车数据计数模型.
  • 和搜索证明了强大的收与低超参数灵敏度.
  • 结果是健全和一致的,表现优于已公布的模型,并降低了开发成本.

结论:

  • 优化框架系统地解决了崩分析中的多个建模决策和目标.
  • 该MetaCountRegressor Python包为研究人员和从业人员提供了一个有价值的工具.
  • 这种方法导致更可靠,可转移和更具成本效益的碰撞频率模型.