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Coefficient of Correlation01:12

Coefficient of Correlation

8.2K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
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Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

355
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
355
Correlation and Regression00:53

Correlation and Regression

3.0K
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...
3.0K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

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The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
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Multiple Regression01:25

Multiple Regression

3.7K
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...
3.7K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

7.7K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
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Updated: Jan 8, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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株価相関分析のためのマルチファクター動的時間系列測定基準

Jinyu Fan1,2, Guanyu Lu3, Jun Ma1,2

  • 1Qinghai Normal University, Xining, China.

PloS one
|December 15, 2025
PubMed
まとめ
この要約は機械生成です。

本研究では、多次元データと時間遅延効果を考慮することで株価相関分析を改善する新しいマルチファクター動的時間類似性尺度（MFDTSM）を導入します。この新しい手法は、業界相関、線形相関、および価格相関における精度を向上させます。

キーワード:
株価相関分析動的時間類似性尺度XGBoostSHAP時間遅延効果

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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科学分野:

  • 定量的金融
  • 計算経済学
  • データサイエンス

背景:

  • 従来の株価相関分析では、株価データの多次元性や動的な時間遅延効果（TLE）を捉えきれないことがよくあります。
  • 既存の類似性尺度は、時間とともに株価の挙動に影響を与える要因の複雑な相互作用に対処するには洗練さが不足しています。

研究 の 目的:

  • より正確な株価相関分析のための新しいマルチファクター動的時間類似性尺度（MFDTSM）を提案すること。
  • 既存の手法が多次元株価データと位相差におけるTLEを処理する上での限界に対処すること。

主な方法:

  • 株価要因の影響を評価するために、Shapley Additive exPlanations（SHAP）と統合された拡張eXtreme Gradient Boosting（XGBoost）モデルを開発しました。
  • SHAP値のクラスタリングを使用して株価の分類と要因の異質性の分析を行いました。
  • 累積距離行列と最適な時系列配置パスを使用してTLE位相差を定量化しました。

主要な成果:

  • MFDTSM手法は、既存の手法と比較して、業界相関（10％）、線形相関（16％）、株価相関価格設定（5％）において精度が向上したことを示しました。
  • 株価を効果的に分類し、要因の影響における異質性を明らかにしました。
  • TLEの動的な位相差を定量化し、類似性尺度の精度を向上させました。

結論:

  • MFDTSMは、多次元データとTLEを組み込むことにより、複雑な株価市場のダイナミクスを分析する上で重要な進歩を提供します。
  • この手法は効率的かつ安定しており、さまざまな相関分析において既存の手法を上回っています。
  • 堅牢な株価市場の洞察を得るためには、動的な時間的側面と要因の相互作用を考慮することの重要性を強調しています。