Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces neutrosophic statistics for survey sampling with uncertain costs. New methods improve estimate precision and resource efficiency in complex surveys.
Area Of Science
- Statistics
- Survey Methodology
- Data Science
Background
- Classical statistics struggle with uncertain data in complex surveys.
- Neutrosophic statistics handle indeterminate and fuzzy information using neutrosophic numbers.
- Existing methods lack robust handling of uncertainty in survey sampling allocation.
Purpose Of The Study
- To address the compromise optimum allocation problem in multi-character stratified random sampling with uncertain per unit measurement costs.
- To develop novel methods for estimating population means of neutrosophic study variables.
- To model fuzzy uncertainty in stratum per unit measurement costs using an intuitionistic fuzzy cost function.
Main Methods
- Formulation of the compromise optimum allocation as a multi-objective intuitionistic fuzzy optimization problem.
- Application of neutrosophic fuzzy programming and intuitionistic fuzzy programming for solution methodology.
- Utilizing Python for statistical analysis and GAMS for numerical optimization.
Main Results
- Proposed methods yield more precise estimates compared to existing approaches.
- The suggested techniques demonstrate efficient utilization of survey resources.
- Numerical study on atmospheric variables validates the practical applicability and effectiveness.
Conclusions
- Neutrosophic statistics offer a powerful framework for handling uncertainty in complex survey sampling.
- The developed optimization approaches provide superior solutions for allocation problems.
- This research enhances the precision and efficiency of statistical inference in uncertain environments.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
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...
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...
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
In the equation, n is the sample size, ͞x is the sample mean, x̿ is the combined mean for all the observations, k is the number of samples, and s2 is the variance of the sample. It should be noted that the subscript 'i'...
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...