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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...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

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Updated: May 14, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Wavelet-based clustering for mixed-effects functional models in high dimension.

M Giacofci1, S Lambert-Lacroix, G Marot

  • 1Laboratoire LJK, BP 53, Université de Grenoble et CNRS, 38041 Grenoble cedex 9, France. madison.giacofci@imag.fr

Biometrics
|February 6, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel wavelet-based method for high-dimensional curve clustering, effectively handling interindividual variability and irregular data. The approach utilizes wavelet decomposition and thresholding for dimension reduction, enabling robust functional data analysis.

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

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Last Updated: May 14, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Functional Data Analysis
  • Statistical Modeling
  • Signal Processing

Background:

  • Traditional curve clustering methods using splines struggle with high-dimensional and irregular data.
  • Existing techniques are not well-suited for modeling complex functional random effects in large datasets.

Purpose of the Study:

  • To develop a robust method for high-dimensional curve clustering that accommodates interindividual variability.
  • To address limitations of spline-based approaches for irregular and high-dimensional functional data.
  • To provide a flexible framework for analyzing complex functional data, including peak-like signals.

Main Methods:

  • Wavelet decomposition for both fixed and random effects to handle high-dimensional signals.
  • Wavelet thresholding for efficient dimension reduction tailored for multiple curves.
  • A linear mixed-effects model in the wavelet domain for clustering, estimated via an EM-algorithm.

Main Results:

  • The proposed method effectively clusters high-dimensional curves with interindividual variability.
  • Demonstrated ability to model irregular functions within Besov spaces.
  • Successful application to mass spectrometry and comparative genomic hybridization (CGH) data.

Conclusions:

  • The wavelet-based approach offers a powerful solution for high-dimensional curve clustering, outperforming traditional methods.
  • The method provides a unified framework for fixed and random effects in the same functional space.
  • The R package 'curvclust' offers the first public implementation for this type of analysis.