在存在直接效应的情况下,增长混合模型的两步估计器与共变量
Yuqi Liu1, Zsuzsa Bakk1, Ethan M McCormick1
1Methodology and Statistics Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands.
Multivariate behavioral research
|October 22, 2025
概括
这项研究引入了一个强大的两步估计器,用于增长混合模型 (GMMs),以处理具有共变量的复杂模型. 新方法在处理潜在的模型错误规范时,提供了比传统的一步和三步估计器更可靠的结果.
科学领域:
- 统计 统计 统计 统计
- 量化心理学 量化心理学
- 计量经济学 计量经济学
背景情况:
- 增长混合模型 (GMMs) 被广泛用于分析经过时间的未观察到的人口异质性.
- 可以将共变量纳入GMM,以预测潜在的类成员和/或增长轨迹.
- 现有的估计方法可能对模型的错误规范敏感,特别是在复杂的GMM中.
研究的目的:
- 提出和评估一个扩展的两步估计器对GMMs.
- 与一步和三步估计器相比,评估拟议估计器对模型错误规范的稳定性.
- 为了研究不同的估计器在各种模型复杂性,涉及共变量效应的表现.
主要方法:
- 开发了用于GMM的潜在类 (LC) 模型的两步估计器的扩展.
- 进行模拟研究,将拟议的两步估计器与一步和三步估计器进行比较.
- 检查了三个人口模型,其中的共变量预测了LC会员资格,潜伏拦截或两种增长因素.
主要成果:
- 当共变量仅预测LC会员时,所有估计器都在强大的测量模型中表现良好.
- 拟议的两步和三步估计器在共变量影响增长因素时,证明了对错误规范的稳定性.
- 一步估计器对错误规范最敏感,而两步和三步估计器倾向于低估标准错误.
结论:
- 扩展的两步估计器为具有共变量的GMM提供了一个强大的替代方案,特别是当模型错误规范是一个问题时.
- 研究人员应该考虑错误规范对复杂GMM估计器性能的潜在影响.
- 拟议的方法在出现对增长轨迹的共同变量效应的场景中提供了更可靠的估计.
相关概念视频
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
1.1K
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...
On...
1.1K
Exponential Equations for Modeling Growth
219
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
219
Parametric Survival Analysis: Weibull and Exponential Methods
1.0K
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...
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...
1.0K
Estimating Population Mean with Unknown Standard Deviation
8.8K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
8.8K
Modeling with Differential Equations
4
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
4
Distributions to Estimate Population Parameter
5.0K
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...
5.0K


