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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.
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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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对固定效应的一参逻辑正指数模型的识别性分析.

Jorge González1,2,3, Jorge Bazán4, Mariana Curi4

  • 1Millennium Nucleus on Intergenerational Mobility: From Modelling to Policy (MOVI), Chile.

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概括

用于不对称的项目特征曲线的逻辑正指数 (LPE) 模型,在它的1PL-LPE形式中无法识别. 这项研究分析了其可识别性,并提供了实际的解决方案.

关键词:
不对称的IRT模型可以识别的可识别性后勤积极指数 (LPE) 模型模型的积极指数.

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科学领域:

  • 心理测量 心理测量 心理测量
  • 教育测量教育的测量
  • 统计建模 统计建模

背景情况:

  • 后勤正指数 (LPE) 模型扩展了两参数后勤 (2PL) 模型,包括对不对称的项目特征曲线的项目参数.
  • 虽然LPE模型显示经验效用,但它们的正式识别性尚未确定.
  • 项目响应理论 (IRT) 模型需要识别性分析来确保参数估计是有效的.

研究的目的:

  • 为了正式确定固定效果IRT模型的未识别状态,包括LPE变体.
  • 为了对1PL-LPE模型进行识别分析,LPE模型的特定版本.
  • 探索其他LPE模型版本的实际应用和影响.

主要方法:

  • 在一类固定效果的IRT模型中正式确定不可识别的条件.
  • 在1PL-LPE模型上进行详细的识别分析.
  • 讨论解决可识别性问题的策略.

主要成果:

  • 基于一参数物流模型 (1PL) 的1PL-LPE模型被证明是不可识别的.
  • 一个广泛的固定效果IRT模型类别,包括LPE模型,被证明具有未识别的状态.
  • 该研究提供了对导致不可识别的条件的见解.

结论:

  • 制定的1PL-LPE模型是不可识别的,需要为实际使用进行调整.
  • 了解模型可识别性对于LPE等先进的IRT模型的有效应用至关重要.
  • 进一步的研究可以探索对LPE模型的修改,以确保可识别性和实用性.