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相关概念视频

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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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Multiple Regression01:25

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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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...
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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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Updated: Jul 25, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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在joinpoint回归中对模型选择方法的数据驱动选择.

Hyune-Ju Kim1, Huann-Sheng Chen2, Douglas Midthune3

  • 1Department of Mathematics, Syracuse University, Syracuse, NY, USA.

Journal of applied statistics
|June 28, 2023
PubMed
概括
此摘要是机器生成的。

在细分回归中选择变化点的新方法提高了趋势分析的效率. 这种方法使用修改的贝叶斯信息标准 (BIC) 进行准确的癌症趋势分析,优于现有方法.

关键词:
贝叶斯信息标准是贝叶斯信息标准.变化点是一个变化点.正确选择的概率.分段线回归的细分线回归.有权重的加权.

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科学领域:

  • 统计 统计 统计 统计
  • 生物统计学 生物统计学
  • 流行病学 流行病学

背景情况:

  • 选择变化点的数量对于细分线回归和趋势分析至关重要.
  • 现有的方法,比如Joinpoint软件中的排列测试,可能是计算密集的.

研究的目的:

  • 提出一种计算效率高,准确的方法来确定细分回归中的变化点数.
  • 将新方法的性能与现有的模型选择程序进行比较.

主要方法:

  • 开发了一种基于两个施瓦茨类型标准的新型模型选择规则:经典的贝叶斯信息标准 (BIC) 和一个更严厉的惩罚的修改版.
  • 该方法使用部分数学来动态权衡BIC和基于效果大小的修改标准.
  • 通过模拟评估了该方法的性能,并将其与换测试程序进行了比较.

主要成果:

  • 拟议的方法在计算上显著更高效,而不是排列测试程序.
  • 模拟表明,新方法保持了正确选择的高概率,与现有方法相比或优于现有方法.
  • 该方法在其他方法失败的场景中表现得更好.

结论:

  • 提出的基于BIC的方法为细分回归中的变化点选择提供了一个高效和有效的替代方案.
  • 这种方法为趋势分析提供了有价值的工具,特别是在癌症流行病学等领域.
  • 该方法已成功应用于分析美国前列腺癌发病率和死亡率.