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相关概念视频

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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...
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Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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State Space Representation01:27

State Space Representation

203
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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相关实验视频

Updated: Jun 24, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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常规结果状态空间模型用于密集的纵向数据.

Teague R Henry1, Lindley R Slipetz2, Ami Falk2

  • 1Department of Psychology and School of Data Science, University of Virginia, Charlottesville, USA. ycp6wm@virginia.edu.

Psychometrika
|June 11, 2024
PubMed
概括

新的状态空间模型准确地分析顺序密集的纵向 (IL) 数据,与线性近似不同. 这提高了通过日常日记和生态瞬间评估捕捉到的心理动态的理解.

关键词:
生态瞬间评估 环境瞬间评估强烈的纵向数据密集.项目响应理论是物品响应理论.顺序测量是指顺序测量.颗粒过器 颗粒过器状态空间建模 状态空间建模

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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科学领域:

  • 心理学科学 心理学科学
  • 量化心理学 量化心理学
  • 心理测量 心理测量 心理测量

背景情况:

  • 密集的纵向 (IL) 数据,收集频繁 (例如,日常日记,生态瞬间评估),对于理解心理动态至关重要.
  • 状态空间建模是分析IL数据的强大框架,但传统上需要连续测量.
  • 心理学研究通常涉及顺序数据 (例如,利克特尺度),这对现有的状态空间模型构成了挑战.

研究的目的:

  • 为容纳顺序测量的状态空间模型开发一个一般的估计方法.
  • 在状态空间分析中使用分级响应模型来具体处理利克特尺度数据.
  • 为了比较新的顺序模型与传统的线性近似方法的性能.

主要方法:

  • 开发了一种用于顺序数据的新型状态空间建模方法,结合了分级响应模型.
  • 采用模拟研究来评估拟议模型的准确性和偏差.
  • 将拟议模型的参数估计和状态动态与线性近似方法进行比较.

主要成果:

  • 建议使用顺序测量的状态空间模型产生了对状态动态的公正估计.
  • 传统的线性近似方法,将顺序数据视为连续的数据,产生了明显偏差的估计.
  • 引入了大致置信区间的"切片标准误差",并指出它们往往比真正的标准误差更自由 (更小).

结论:

  • 开发的状态空间模型为使用顺序测量的强度纵向数据提供了准确的估计.
  • 在状态空间模型中将顺序数据视为连续的处理可能会导致心理研究中的大量偏差.
  • 新的方法提高了复杂的心理过程的分析,使用易于获得的顺序数据格式.