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相关概念视频

Quantitative Analysis01:12

Quantitative Analysis

307
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
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Time-Series Graph00:54

Time-Series Graph

4.4K
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...
4.4K
Relative Frequency Histogram01:14

Relative Frequency Histogram

5.5K
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...
5.5K
Econometric Views (EViews)01:29

Econometric Views (EViews)

145
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
145
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

779
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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相关实验视频

Updated: Jul 5, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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学习金融网络与高频贸易数据.

Kara Karpman1, Sumanta Basu1, David Easley2

  • 1Department of Statistics and Data Science, Cornell University.

Data science in science
|January 22, 2024
PubMed
概括

本研究使用机器学习从高频交易数据构建金融网络. 它揭示了网络密度在2007-09金融危机之前达到顶峰,较大的公司显示出更强大的预测能力.

科学领域:

  • 量化金融 量化金融
  • 机器学习 机器学习
  • 金融计量经济学 金融计量经济学

背景情况:

  • 传统的金融网络分析依赖于低频数据,限制了对市场动态的洞察力.
  • 高频交易数据提供了更丰富的信息,但提出了重要的建模挑战 (异步,非静止).
  • 现有的方法很难充分利用当天交易数据的细节性来进行网络估计.

研究的目的:

  • 开发一种新的方法,用高频率的日内交易数据来估计金融网络.
  • 应用机器学习,特别是随机森林,以克服高频数据的挑战.
  • 分析金融网络连接的演变导致2007-09年美国金融危机.

主要方法:

  • 利用随机森林,一种机器学习算法,在没有广泛的超参数调整的情况下进行强大的网络估计.
  • 通过预测一个公司使用另一个公司的微观结构数据的市场指标 (例如,实现的波动性) 的变化来定义网络边缘.
  • 研究了网络演变和企业级联网模式.

主要成果:

  • 2007年,金融网络密度在美国金融危机之前是最高的.
  • 雷曼兄弟在2006年表现出高连接性,表明其核心作用.
  • 较大的公司在网络链接方面表现出更强的预测能力,与市场微观结构理论保持一致.
关键词:
高频交易是一种高频交易.市场微观结构 市场微观结构随机的森林随机的森林

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结论:

  • 随机森林提供了一种有效的方法,可以从高频数据中建模复杂的金融网络.
  • 该研究强调了网络的动态变化和金融危机发生前期公司的关键角色.
  • 调查结果强调了企业规模在确定金融网络内的预测影响的重要性.