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Cluster Sampling Method01:20

Cluster Sampling Method

12.7K
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...
12.7K
Time-Series Graph00:54

Time-Series Graph

4.5K
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...
4.5K
Relative Frequency Histogram01:14

Relative Frequency Histogram

5.6K
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...
5.6K
Survival Tree01:19

Survival Tree

157
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.
 Building a Survival Tree
Constructing a...
157
Probability Histograms01:17

Probability Histograms

12.2K
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.
12.2K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

140
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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
140

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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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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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繰り返し発生するイベントデータのクラスタリング

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
まとめ

この研究は,繰り返し発生するイベントを分析するための新しい混合モデルを導入し,観察されていない異質性を効果的に対処します. このモデルは,医療や産業におけるイベントのダイナミクスをより深く理解するために,個人をクラスタ化することを可能にします.

キーワード:
アンダーセン・ギルモデルEMアルゴリズム繰り返し発生するデータ医学への応用モデルベースのクラスタリング

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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Automated Detection and Analysis of Exocytosis
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Automated Detection and Analysis of Exocytosis

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関連する実験動画

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

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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Automated Detection and Analysis of Exocytosis
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科学分野:

  • バイオ統計学
  • 生存分析
  • 機械学習

背景:

  • タイムスタンプされたデータは,総数を超えて繰り返されるイベントのダイナミクスをモデル化する必要があります.
  • アンダーセン・ギルとコックスのような既存のモデルは 観察されていない異質性と闘っています
  • 病院再入院,再発,産業の失敗などがあります.

研究 の 目的:

  • 繰り返し発生するイベントの混合モデルを提案し,観察されていない異質性を説明する.
  • 潜伏変数に基づく個体の無監督分類 (クラスタリング) を可能にする.
  • 異なるクラスター内の繰り返し発生するプロセスの詳細な理解を図る.

主な方法:

  • 繰り返し発生するイベントデータのための混合モデルを開発しました.
  • 各クラスター内の共変数に調整されたパラメトリック強度仕様.
  • Expectation-Maximization (EM) アルゴリズムによる最大確率の推定を用いた.
  • ベイジアン情報基準 (BIC) を最適クラスター番号の選択に利用した.

主要な成果:

  • シミュレーションデータを用いてモデルの実現可能性を示した.
  • 高齢者の再入院データに モデルを適用しました
  • 繰り返し起こる出来事のパターンに基づいて 個々の集団を成功裏に特定し,特徴づけました.

結論:

  • 提案された混合モデルでは,繰り返し発生するデータにおける未確認の異質性を効果的に処理します.
  • クラスタリングは 患者の異なるサブグループとそのイベントダイナミクスに関する貴重な洞察を提供します.
  • このモデルは,様々な分野における複合的な繰り返しイベントデータを分析するための強力なツールを提供します.