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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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...
Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

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Related Experiment Video

Updated: Jun 18, 2026

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

Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables.

Benhuai Xie1, Wei Pan, Xiaotong Shen

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, benhuaix@biostat.umn.edu.

Electronic Journal of Statistics
|November 19, 2009
PubMed
Summary

This study introduces a novel clustering method for high-dimensional data, effectively performing variable selection and parameter estimation. The approach handles cluster-specific covariance matrices and allows for grouped variable selection, improving accuracy in complex datasets.

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

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Last Updated: Jun 18, 2026

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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

  • Statistics
  • Bioinformatics
  • Machine Learning

Background:

  • Clustering analysis is crucial for high-dimensional data, such as microarray analysis.
  • Noise variables in high-dimensional data can obscure underlying clustering structures.
  • Existing methods often assume a common covariance matrix, which is not always practical.

Purpose of the Study:

  • To introduce a novel approach for simultaneous variable selection and parameter estimation in model-based clustering.
  • To address limitations of existing methods by allowing cluster-specific covariance matrices.
  • To enable grouped variable selection for incorporating prior knowledge, like gene functions.

Main Methods:

  • A novel penalized likelihood approach is proposed for high-dimensional data.
  • The method shrinks variances and means, accommodating cluster-specific diagonal covariance matrices.
  • Expectation-Maximization (EM) algorithms are derived for parameter estimation.

Main Results:

  • The proposed method effectively performs simultaneous variable selection and parameter estimation.
  • Shrinkage and thresholding effects are evident in the EM algorithm's M-steps.
  • The approach demonstrated utility in a case study of acute leukemia subtype discovery using microarray data.

Conclusions:

  • The novel clustering approach offers advantages for analyzing high-dimensional data, especially with low sample sizes.
  • It provides a more flexible framework by allowing cluster-specific covariance matrices.
  • The method facilitates incorporating biological knowledge through grouped variable selection, aiding in disease subtype discovery.