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相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Estimating Population Mean with Unknown Standard Deviation01:22

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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相关实验视频

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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在比例平均模型下的混合面板计数数据的变量选择.

Lei Ge1, Baosheng Liang2, Tao Hu3

  • 1Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.

Statistical methods in medical research
|July 4, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的惩罚性概率方法,用于混合面板计数数据的事件历史研究中的变量选择. 该方法有效地识别复杂的医学研究场景中的风险因素.

关键词:
预期最大化算法算法医学上的不坚持是医疗上的不坚持.混合面板计数数据数据 混合面板计数数据比例平均模型的比例平均模型.选择变量的选择变量.

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科学领域:

  • 生物统计学 生物统计学
  • 医疗信息学 医疗信息学
  • 流行病学 流行病学

背景情况:

  • 在医学研究中常见的混合面板计数数据,为事件历史分析带来了独特的挑战.
  • 现有的变量选择程序对于这些复杂的数据类型是不够的.

研究的目的:

  • 为使用混合面板计数数据进行事件历史研究提出一种新的惩罚性概率变量选择程序.
  • 用坐标下降的预期最大化算法开发一个高效的实现.
  • 验证方法的性能和理论特性.

主要方法:

  • 对于变量选择的惩罚性概率方法.
  • 一个预期最大化 (EM) 算法与坐标下降集成用于参数估计.
  • 为拟议方法建立预言属性的理论建立.
  • 模拟研究来评估实际性能.

主要成果:

  • 拟议的惩罚性概率方法在混合面板计数数据的变量选择中表现出有效性.
  • 模拟结果证实了该方法在实际环境中的强大性能.
  • 该方法的预言属性在理论上已经得到证实.

结论:

  • 开发的惩罚性概率方法为复杂事件历史研究中的变量选择提供了可靠的方法.
  • 该方法在一项临床研究中成功应用于识别医疗不遵守的风险因素.
  • 这项工作为医学研究中分析混合面板计数数据提供了有价值的工具.