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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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...
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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 squares (OLS)...
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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.
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Bayesian model selection for incomplete data using the posterior predictive distribution.

Michael J Daniels1, Arkendu S Chatterjee, Chenguang Wang

  • 1Department of Statistics, University of Florida, Gainesville, FL 32611, USA. mdaniels@stat.ufl.edu

Biometrics
|May 4, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new posterior predictive loss criterion for selecting models with incomplete longitudinal data. While the deviance information criterion (DIC) generally performs better, the proposed method is simpler to compute for certain missing data models.

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Model selection for incomplete longitudinal data is crucial for accurate analysis.
  • Existing criteria may not adequately address the complexities of missing data.
  • The Gelfand and Ghosh criterion extension presents computational and theoretical challenges.

Purpose of the Study:

  • To identify essential properties for model selection criteria in incomplete data scenarios.
  • To propose and evaluate a novel posterior predictive loss criterion for incomplete longitudinal data.
  • To compare the performance of the proposed criterion against the deviance information criterion (DIC).

Main Methods:

  • Identification of a key property for incomplete data model selection criteria.
  • Critique of a direct extension of the Gelfand and Ghosh criterion.
  • Development and simulation-based evaluation of an alternative posterior predictive loss criterion.
  • Comparison with the deviance information criterion (DIC) using simulations and a real dataset.

Main Results:

  • The extended Gelfand and Ghosh criterion exhibits undesirable properties for incomplete data.
  • The proposed posterior predictive criterion demonstrates good overall performance.
  • The deviance information criterion (DIC) generally outperforms the proposed criterion.
  • The proposed criterion offers computational advantages over DIC in specific missing data models.

Conclusions:

  • A new posterior predictive loss criterion offers a viable alternative for model selection with incomplete longitudinal data.
  • While DIC often excels, the proposed criterion provides a computationally efficient option.
  • Further research may refine posterior predictive criteria for enhanced performance in complex missing data situations.