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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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)...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Nonparametric Bayes local partition models for random effects.

David B Dunson1

  • 1Department of Statistical Science, Box 90251, Duke University, Durham, North Carolina 27708, U.S.A.

Biometrika
|May 28, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel prior for Bayesian hierarchical models to achieve sparse representations through combined global and local information sharing. The proposed local partition process prior enables dependent clustering for improved parameter analysis.

Keywords:
Dirichlet processFunctional dataLocal shrinkageMeta-analysisMulti-task learningPartition modelSlice samplingStick-breaking

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Last Updated: May 11, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Statistics
  • Bayesian Inference
  • Machine Learning

Background:

  • Choosing priors for unknown random effects distributions is crucial in Bayesian hierarchical modeling.
  • Existing methods may not effectively capture complex similarities between subjects across parameters.
  • Sparse representations are desirable for efficient analysis of high-dimensional data.

Purpose of the Study:

  • To propose a novel local partition process prior for Bayesian hierarchical models.
  • To enable dependent local clustering for sparse representations.
  • To facilitate flexible borrowing of information at both global and local levels.

Main Methods:

  • Development of a local partition process prior that induces dependent local clustering.
  • Derivation of two-parameter expressions for marginal and conditional clustering probabilities.
  • Implementation of a slice sampler for efficient posterior computation, avoiding approximations of infinite random measures.

Main Results:

  • The local partition process prior effectively clusters subjects for subsets of parameters.
  • The proposed slice sampler allows for accurate posterior computation.
  • Demonstrated utility through simulation studies and a real-world application.

Conclusions:

  • The local partition process prior offers a flexible and effective approach for sparse Bayesian hierarchical modeling.
  • The developed computational methods facilitate practical application of the proposed prior.
  • The approach enhances the understanding of subject similarities in complex data structures.