Jove
Visualize
Contact Us

Related Concept Videos

Survival Tree01:19

Survival Tree

115
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...
115
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

226
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...
226
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.9K
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...
1.9K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

484
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...
484
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
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...
81

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Trap tales: The influence of red alder stand conditions and forest fragmentation on family-level beetle bycatch diversity.

PloS one·2026
Same journal

MamNet-PT: A Mamba-enhanced hybrid architecture with selective state-space modeling for uncertainty-aware brain tumor segmentation.

PloS one·2026
Same journal

Multicenter evaluation of BACT-Info. and an infection algorithm using Urine Flow Cytometry among clinically diagnosed UTI patients in Indonesia.

PloS one·2026
Same journal

Cross-cultural adaptation and psychometric properties study of Prolonged Grief Disorder Questionnaire (PG-12-R) for caregivers of terminal cancer patients, Thai version.

PloS one·2026
Same journal

Design and in silico validation of donor DNA for RNA-guided recombinase-mediated knockout of mstnb gene in Labeo rohita.

PloS one·2026
Same journal

ViT-MultiRAGNet: A scalable and reliable retrieval-augmented Vision Transformer framework for memory-guided feature fusion multi-modal mammogram classification.

PloS one·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 23, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Enhancing forecast accuracy using combination methods for the hierarchical time series approach.

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
Summary

Combining hierarchical time series forecasts using the AC method significantly improves accuracy for international trade predictions compared to individual models. This enhances planning for trade balance and production policies.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

10.7K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K

Related Experiment Videos

Last Updated: Jul 23, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

10.7K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K

Area of Science:

  • Econometrics
  • Time Series Analysis
  • International Trade

Background:

  • Accurate forecasting of international trade is crucial for economic planning and policy development.
  • Traditional forecasting models often struggle with the hierarchical nature of trade data (e.g., aggregate vs. disaggregate levels).
  • Hierarchical time series methods offer a structured approach to improve forecast accuracy by considering relationships across different data aggregation levels.

Purpose of the Study:

  • To evaluate if combining forecasts from different models within a hierarchical structure improves accuracy over individual models.
  • To compare various hierarchical forecasting approaches, including bottom-up, top-down, and optimal combination methods.
  • To identify the most effective forecasting and combining methods for international trade data, specifically for Egypt.

Main Methods:

  • Employed Autoregressive Moving Average (ARIMA) and Exponential Smoothing (ETS) models for forecasting at different hierarchy levels.
  • Utilized hierarchical forecasting approaches: bottom-up, top-down, and Minimum Trace Sample estimator (MinT-Sample).
  • Combined forecasts from the best-performing individual hierarchical methods (MinT-Sample and bottom-up with ARIMA) using five different combination techniques.

Main Results:

  • The ARIMA model with MinT-Sample and bottom-up approaches demonstrated superior predictive performance.
  • The Average Combination (AC) method proved superior to other combining methods.
  • Forecasts generated by combining models using the AC method were more accurate than individual models at the aggregate (level zero) and disaggregate (level one) trade levels.

Conclusions:

  • Combining forecasts through hierarchical time series analysis significantly enhances the accuracy of import and export predictions.
  • Accurate trade forecasts can inform strategic planning for improving trade balance and optimizing production policies.
  • Recommends the adoption of hierarchical forecasting methods in international trade for improved precision and policy guidance.