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相关概念视频

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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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

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Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Distribution Reliability and Automation01:25

Distribution Reliability and Automation

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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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相关实验视频

Updated: Jul 9, 2025

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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使用交叉验证方法来选择时间序列模型:承诺和陷.

Siwei Liu1, Di Jody Zhou1

  • 1Human Development and Family Studies, Department of Human Ecology, University of California at Davis, Davis, California, USA.

The British journal of mathematical and statistical psychology
|December 7, 2023
PubMed
概括

交叉验证 (CV) 方法对于评估心理学中的时间序列模型至关重要. 封闭的CV通常优于传统的信息标准,如AIC和BIC,用于评估预测错误,特别是在有限的数据的情况下.

科学领域:

  • 心理学 心理学 心理学
  • 统计 统计 统计 统计
  • 时间序列分析时间序列分析

背景情况:

  • 矢量自回归 (VAR) 建模在心理学中用于时间序列分析是常见的.
  • 心理学研究中的短时间序列往往导致VAR模型过拟合和预测差.
  • 建议进行交叉验证 (CV) 来评估模型的预测能力,但其与心理时间序列数据的性能尚不清楚.

研究的目的:

  • 检查10倍CV和阻塞CV如何估计人均,AR和VAR模型的预测错误.
  • 评估数据特征对CV方法性能的影响.
  • 为了比较CV方法与Akaike (AIC) 和贝叶斯 (BIC) 模型选择信息标准.

主要方法:

  • 模拟研究分析了三个时间序列模型 (人-平均,AR,VAR) 的预测错误.
  • 评估两种交叉验证技术:十倍CV和封闭CV.
  • 将CV方法与传统模型选择标准 (AIC,BIC) 的比较.

主要成果:

  • CV方法显示,对于更简单的模型 (人-平均,AR) 预测错误的低估趋势.
  • CV方法倾向于高估VAR模型的预测错误,特别是在小样本大小的情况下.
  • 封闭的CV在选择最具预测性的模型方面普遍优于AIC和BIC.
关键词:
自动回归模型的自动回归模型进行交叉验证.信息标准 信息标准 信息标准时间序列时间序列矢量自回归模型 矢量自回归模型

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结论:

  • 交叉验证,特别是阻塞的简历,是评估时间序列模型在心理学中的预测准确性的宝贵工具.
  • 在模型选择方面,CV方法比AIC和BIC具有优势,尽管样本规模小,可能存在偏差.
  • 为心理时间序列分析中CV的实际应用提供了指导方针.