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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
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在组成数据回归中贝叶斯变量收缩和选择:对口腔微生物群的应用

Jyotishka Datta1, Dipankar Bandyopadhyay2

  • 1Department of Statistics, Virginia Polytechnic Institute and State University, 250 Drillfield Drive, Blacksburg, VA 24061 USA.

Journal of the Indian Society for Probability and Statistics
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概括
此摘要是机器生成的。

这项研究引入了贝叶斯的方法,使用连续收缩先验进行微生物组数据分析. 这些方法有效地确定组合数据中协变量和分类学丰度之间的显著关联.

关键词:
贝叶斯语 贝叶斯语 贝叶斯语 贝叶斯语组合数据是指组成的数据.迪里克莱特 (Dirichlet) 是一个一般化的迪里克莱特 (Dirichlet)马的马是什么意思大的 p 大的 p在收缩之前的收缩.稀疏的概率向量是什么打破棍棒的方法

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科学领域:

  • 微生物学 微生物学
  • 统计建模 统计建模
  • 生物信息学是一种生物信息学.

背景情况:

  • 微生物组研究产生复杂的组成数据 (例如,种群数量),这些数据是非负的,有界的,和和为一个.
  • 迪里克莱多项式回归 (D-M) 是用共变量建模此类数据的常用框架.
  • 有效的变量选择对于识别高维微生物组数据中的关键关联至关重要.

研究的目的:

  • 介绍一个贝叶斯的方法,用于迪里克莱特多项式组合数据分析.
  • 为了比较连续收缩先验 (马,马+) 与贝叶斯拉索对变量选择的性能.
  • 在微生物组数据中确定共变量和分类学丰度之间的显著关联.

主要方法:

  • 在D-M回归框架下贝叶斯估计和推断.
  • 马,马+和贝叶斯拉索先验的比较评估.
  • 利用哈密尔顿式蒙特卡洛来进行后续采样,并生成可信的间隔.
  • 应用于模拟研究和真实口腔微生物组数据的合成数据的方法 (NYC-Hanes研究).

主要成果:

  • 连续收缩先验在稀疏的参数设置中显示出出色的恢复和估计准确性.
  • 马和马+先验在识别显著的共变量-税率关联方面表现强.
  • 这种方法在口腔微生物组数据上得到了成功的说明.

结论:

  • 贝叶斯连续收缩先验为D-M组合微生物组数据中的变量选择提供了一个有效的策略.
  • 提出的方法提高了识别微生物群落组成的关键驱动因素的能力.
  • 一个RStan实现可用于更广泛的应用.