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Related Concept Videos

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Cluster Sampling Method

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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.
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Related Experiment Video

Updated: Sep 9, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Clustering of recurrent events data.

G Babykina1, V Vandewalle2, J Carretero-Bravo3,4

  • 1ULR 2694 - METRICS - Évaluation des Technologies de Santé et des Pratiques Médicales, CHU Lille, Université de Lille, Lille, France.

Journal of Applied Statistics
|September 4, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mixture model for analyzing recurrent events, effectively addressing unobserved heterogeneity. The model enables clustering individuals for a deeper understanding of event dynamics in healthcare and industry.

Keywords:
Andersen–Gill modelEM algorithmRecurrent events dataapplication to medicinemodel-based clustering

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Machine Learning

Background:

  • Timestamped data necessitates modeling recurrent event dynamics beyond total counts.
  • Existing models like Andersen-Gill and Cox struggle with unobserved heterogeneity.
  • Applications include hospital readmissions, disease recurrences, and industrial failures.

Purpose of the Study:

  • To propose a mixture model for recurrent events to account for unobserved heterogeneity.
  • To enable unsupervised classification (clustering) of individuals based on latent variables.
  • To provide a fine-grained understanding of recurrent event processes within distinct clusters.

Main Methods:

  • Developed a mixture model for recurrent event data.
  • Incorporated parametric intensity specification adjusted for covariates within each cluster.
  • Employed maximum likelihood estimation via the Expectation-Maximization (EM) algorithm.
  • Utilized the Bayesian Information Criterion (BIC) for optimal cluster number selection.

Main Results:

  • Demonstrated model feasibility using simulated data.
  • Applied the model to real-world hospital readmission data for the elderly.
  • Successfully identified and characterized distinct clusters of individuals based on recurrent event patterns.

Conclusions:

  • The proposed mixture model effectively handles unobserved heterogeneity in recurrent event data.
  • Clustering provides valuable insights into distinct patient subgroups and their event dynamics.
  • The model offers a powerful tool for analyzing complex recurrent event data in various fields.