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関連する概念動画

Regression Toward the Mean01:52

Regression Toward the Mean

7.0K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Multiple Regression01:25

Multiple Regression

4.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.0K
Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.4K
Regression Analysis01:11

Regression Analysis

8.4K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

21.6K
The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion....
21.6K
Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

5.1K
The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
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関連する実験動画

Updated: Feb 4, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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触媒事前分布を用いたコックス回帰モデルのベイズ推論

Weihao Li1, Dongming Huang1

  • 1Department of Statistics & Data Science, National University of Singapore, 117546, Singapore.

Biometrics
|February 3, 2026
PubMed
まとめ

コックス触媒事前分布をコックスモデルのベイズ推論のために導入し、少サンプルサイズでの安定性を向上させる。この手法は、標準的な推論手法に代わる頑健な選択肢を提供することで、生存データ解析を強化する。

科学分野:

  • 統計学
  • 生物統計学
  • 生存分析

背景:

  • コックス比例ハザードモデル(コックスモデル)は、生存データに広く使用されている。
  • コックスモデルにおける標準的な推論方法は、モデル次元に対するサンプルサイズが小さい場合に課題に直面する。
  • 既存の方法では、高次元設定における複雑なパラメトリックモデルを十分に安定化できない場合がある。

主な方法:

  • 合成データと代理のベースラインハザードを用いたコックス触媒事前分布の定式化。
  • より単純な適合モデルの予測分布からの合成データの生成。
  • 正則化された対数部分尤度推定量としての近似周辺事後モードの導出。

結論:

  • コックス触媒事前分布は、特に困難な小さいサンプルサイズシナリオにおいて、コックスモデル推論のための頑健で効果的なベイズアプローチを提供する。
  • この方法は、従来の技術を上回る、安定した一貫性のある推定量を提供する。
  • このアプローチは、実世界の生存データ解析に適用可能である。
キーワード:
事前分布の指定比例ハザードモデル正則化安定推定合成データ

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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