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相关概念视频

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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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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Updated: Jun 13, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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基于区块链和参考面板的估计器用于大尺寸的遗传数据预测.

Bingxin Zhao1, Shurong Zheng2, Hongtu Zhu3

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

Annals of statistics
|September 16, 2024
PubMed
概括

区块基因预测方法可能不如全共变矩阵方法准确,即使已知链接不平衡 (LD) 区块结构. 在高维基遗传预测中,训练数据和外部参考面板之间的性能不同.

关键词:
区块对角的共变矩阵.初级 62J0505 的情况.高维度预测的预测.链接不平衡 关系不平衡随机矩阵理论是随机矩阵理论.参考面板的参考面板是一个参考面板.二次的 60B20 二次的 60B20

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科学领域:

  • 遗传学 是一个遗传学.
  • 统计遗传学 统计遗传学
  • 生物信息学是一种生物信息学.

背景情况:

  • 遗传预测旨在将遗传发现转化为医学进步.
  • 高维基遗传数据通常表现出由于链接不平衡 (LD) 的块对角共变性结构.
  • 由于隐私问题,当前的方法经常使用外部参考面板来估计LD块内的变量依赖.

研究的目的:

  • 在高维基遗传预测中,提供基于区块和参考面板的估计器的统一分析.
  • 在没有稀疏性假设的块对角共变性结构下调查不同估计方法的准确性.
  • 使用原始培训数据与外部参考面板对比方法的性能.

主要方法:

  • 开发了一个统一的理论框架来分析基于区块和参考面板的估计器.
  • 将随机矩阵理论的新成果应用于高维块对角线共变矩阵.
  • 通过模拟和来自英国生物银行的真实世界数据进行了广泛的数值评估.

主要成果:

  • 令人惊的是,区块式估计方法可能比全共变矩阵方法更不准确,即使具有清晰的LD区块结构.
  • 基于外部参考面板的估计方法与使用高维度原始训练数据的估计方法相比,可能表现不同.
  • 绩效差异突出了与仅使用培训集中的总结级数据相关的潜在成本.

结论:

  • 重新考虑在遗传预测中依赖严格的区块式估计是有必要的.
  • 在使用原始培训数据和外部参考面板之间做出选择会对预测准确性产生重大影响.
  • 进一步研究强大的高维共差估计对于推进遗传预测至关重要.