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相关概念视频

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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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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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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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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提高预测准确度,使用对等级时间序列方法的组合方法.

Rania A H Mohamed1

  • 1Department of Statistics, Mathematics, and Insurance, Faculty of Commerce, Port Said University, Port Fouad, Port Said, Egypt.

PloS one
|July 17, 2023
PubMed
概括

结合使用AC方法的等级时间序列预测,与单个模型相比,大大提高了国际贸易预测的准确性. 这有助于规划贸易平衡和生产政策.

科学领域:

  • 计量经济学 计量经济学
  • 时间序列分析时间序列分析
  • 国际贸易 国际贸易 国际贸易

背景情况:

  • 准确预测国际贸易对于经济规划和政策制定至关重要.
  • 传统的预测模型经常与贸易数据的等级性质 (例如,汇总与分类水平) 斗争.
  • 层次时间序列方法提供了一个结构化的方法,通过考虑不同数据聚合级别之间的关系来提高预测准确性.

研究的目的:

  • 评估在等级结构中将不同模型的预测结合起来,是否能比单个模型提高准确性.
  • 为了比较各种等级预测方法,包括自下而上,自上而下和最佳组合方法.
  • 确定最有效的预测和结合方法,用于国际贸易数据,特别是埃及.

主要方法:

  • 采用自行回归移动平均 (ARIMA) 和指数平滑 (ETS) 模型用于不同层次层级的预测.
  • 使用等级预测方法:自下而上,自上而下和最小痕迹样本估计器 (MinT-Sample).
  • 使用五种不同的组合技术,从表现最好的单个层次方法 (MinT-Sample和ARIMA的自下而上的) 结合预测.

主要成果:

  • 使用MinT-Sample和自下而上的方法的ARIMA模型显示出卓越的预测性能.
  • 平均组合 (AC) 方法被证明优于其他组合方法.

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  • 使用AC方法组合模型生成的预测在汇总 (零级) 和分类 (一级) 贸易水平上比单个模型更准确.

    • 结论:

    • 通过分层时间序列分析将预测结合在一起,可以显著提高进出口预测的准确性.
    • 准确的贸易预测可以为改善贸易平衡和优化生产政策的战略规划提供信息.
    • 建议在国际贸易中采用层次预测方法,以提高准确性和政策指导.