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相关概念视频

Prediction Intervals01:03

Prediction Intervals

2.2K
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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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

423
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...
423
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

64
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...
64
Randomized Experiments01:13

Randomized Experiments

6.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.8K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.6K
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...
7.6K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K

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相关实验视频

Updated: Jun 13, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

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用GPU加速估计共享随机效应联合模型,用于动态预测.

Shikun Wang1, Zhao Li2, Lan Lan2

  • 1Department of Biostatistics, the University of Texas MD Anderson Cancer Center, United States.

Computational statistics & data analysis
|September 11, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的两阶段估计程序和GPU编程,以加快纵向和生存数据的联合模型. 该方法通过考虑非线性预测器轨迹来提高预测准确度,特别是在大型数据集中.

关键词:
图形处理单元 (GPU) 计算计算.电子健康记录是电子健康记录.联合建模 联合建模纵向和生存数据.数字集成是一个数字集成.平行计算是平行计算中的一个.

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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相关实验视频

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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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科学领域:

  • 生物统计学 生物统计学
  • 计算生物学 计算生物学
  • 流行病学 流行病学

背景情况:

  • 在队列研究中,使用纵向数据预测临床事件风险至关重要.
  • 随着时间的推移,风险因素及其关联的动态变化使准确的预测变得复杂.
  • 对于纵向和生存数据的现有联合模型面临着计算方面的挑战,特别是对于大型数据集和非线性轨迹.

研究的目的:

  • 开发一种计算效率高的方法,用于对纵向和生存数据的联合建模.
  • 通过在纵向预测器中纳入非线性来提高风险预测的准确性.
  • 为了利用图形处理单元 (GPU) 编程来更快地进行模型估计.

主要方法:

  • 开发了一种新的两阶段估计程序.
  • 通过PyTorch实现的图形处理单元 (GPU) 编程被用来加速计算.
  • 拟议的方法通过对大型数据集的数值研究进行了评估.

主要成果:

  • 拟议的算法和软件显著减少了联合模型的估计时间.
  • 考虑到纵向预测轨迹中的非线性,与忽视非线性模型相比,提高了预测准确性.
  • 计算速度提升对于大型数据集来说尤其显著.

结论:

  • 开发的方法为纵向研究中的联合建模提供了一种计算效率高,准确的方法.
  • GPU编程和两阶段估计程序有效地解决了复杂的联合模型的计算负担.
  • 对非线性预测器轨迹的准确建模对于改善临床事件风险预测至关重要.