Jove
Visualize
Contact Us
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 Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

13.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.0K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.3K
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...
4.3K
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

170
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...
170
Probability Distributions01:32

Probability Distributions

8.6K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
8.6K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

136
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...
136
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

109
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...
109

You might also read

Related Articles

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

Sort by
Same author

Single-cell chromatin accessibility and transcriptomic characterization of Behcet's disease.

Communications biology·2023
Same author

Deleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality.

Nature communications·2023
Same author

Short-interval second ejaculation improves sperm quality, blastocyst formation in oligoasthenozoospermic males in ICSI cycles: a time-lapse sibling oocytes study.

Frontiers in endocrinology·2023
Same author

The prevalence of imposter syndrome and associated factors in Chinese medical students and residents: A single-center pilot study.

Medical teacher·2023
Same author

A stent spat up: can EUS-guided biliary drainage stents be removed now that cancer patients live longer?

Endoscopic ultrasound·2023
Same author

The protein arginine methyltransferase family (PRMTs) regulates metastases in various tumors: From experimental study to clinical application.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2023

Related Experiment Video

Updated: Sep 30, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

An estimation of distribution algorithm with clustering for scenario-based robust financial optimization.

Wen Shi1, Xiao-Min Hu2, Wei-Neng Chen1,3

  • 1School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.

Complex & Intelligent Systems
|March 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm (NSEDA-C) for robust financial optimization, addressing scenario-based uncertainty in investment planning. The algorithm effectively balances investment returns and risks, as demonstrated in a group insurance portfolio problem.

Keywords:
Estimation of distribution algorithmFinancial investmentMulti-objective optimizationUncertainty handling

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

675
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Related Experiment Videos

Last Updated: Sep 30, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

675
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Area of Science:

  • Financial mathematics
  • Optimization algorithms
  • Risk management

Background:

  • Financial optimization seeks robust investment plans balancing return and risk.
  • Scenario-based uncertainty, where market conditions significantly impact investment performance, is under-explored in multi-objective optimization.
  • Robust financial planning is crucial for maximizing returns while minimizing risk.

Purpose of the Study:

  • To propose a novel algorithm for scenario-based robust financial optimization.
  • To address the under-explored domain of scenario-based uncertainty in multi-objective optimization problems.
  • To evaluate the algorithm's effectiveness on a real-world financial problem.

Main Methods:

  • A nondominated sorting estimation of distribution algorithm with clustering (NSEDA-C) was developed.
  • A simplified simulation method was used to measure investment return.
  • An estimation model was devised to quantify investment risk.
  • The NSEDA-C was applied to a robust group insurance portfolio problem.

Main Results:

  • The proposed NSEDA-C algorithm effectively handles scenario-based uncertainty in financial optimization.
  • The algorithm demonstrated its capability to balance investment returns and risks.
  • Validation was achieved through application to a group insurance portfolio problem with real-world insurance products.

Conclusions:

  • The NSEDA-C is an effective algorithm for solving scenario-based robust financial optimization problems.
  • The study highlights the importance of considering scenario-based uncertainty in financial planning.
  • The proposed method offers a valuable tool for developing robust investment strategies in insurance and finance.