New classes of difference cum-ratio-type exponential estimators for a finite population variance in stratified random sampling

Daraz, Umer; Wu, Jinbiao; Alomair, Mohammed Ahmed; Aldoghan, Luai Abdulla

doi:10.1016/j.heliyon.2024.e33402

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e33402

|View full text |Cite

New classes of difference cum-ratio-type exponential estimators for a finite population variance in stratified random sampling

Umer Daraz,

Jinbiao Wu,

Mohammed Ahmed Alomair

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network

Yue,

Sha,

Sun

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

After rolling, TP2 copper tubes exhibit defects such as sawtooth marks, cracks, and uneven wall thickness after joint drawing, which severely affects the quality of the finished copper tubes. To study the effect of drawing process parameters on wall thickness uniformity, an ultrasonic detection platform for measuring the wall thickness of rolled copper tubes was constructed to verify the accuracy of the experimental equipment. Using the detected data, a finite element model of drawn copper tubes was established, and numerical simulation studies were conducted to analyze the influence of parameters such as outer die taper angle, drawing speed, and friction coefficient on drawing force, maximum temperature, average wall thickness, and wall thickness uniformity. To address the problem of the large number of finite element model meshes and low solution efficiency, the wall thickness uniformity was predicted using a radial basis function (RBF) neural network, and parameter optimization was performed using the particle swarm optimization (PSO) algorithm. The research results show that the RBF neural network can accurately predict wall thickness uniformity, and using the PSO optimization algorithm, the best parameter combination can reduce the wall thickness uniformity after drawing in finite element simulation.

show abstract

Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network

Yue,

Sha,

Sun

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

New Techniques for Estimating Finite Population Variance Using Ranks of Auxiliary Variable in Two-Stage Sampling

Daraz,

Alomair,

Albalawi

et al. 2024

Mathematics

View full text Add to dashboard Cite

This article presents a new set of estimators designed to estimate the finite population variance of a study variable in two-phase sampling. These estimators utilize the information about extreme values and ranks of an auxiliary variable. Through a first-order approximation, we investigate the properties of these estimators, including biases and mean squared errors (MSEs). Furthermore, a comprehensive simulation study is conducted to assess their performance and validate our theoretical insights. Results demonstrate that our proposed class of estimators performs better in terms of percent relative efficiency (PRE) across various simulation scenarios compared to existing estimators. In addition, in the application section, we utilize three data sets to further validate the performance of our proposed estimators against conventional unbiased variance estimators, ratio and regression estimators, as well as other existing methods.

show abstract

Dual Transformation of Auxiliary Variables by Using Outliers in Stratified Random Sampling

Alomair,

Daraz

2024

Mathematics

View full text Add to dashboard Cite

To estimate the finite population variance of the study variable, this paper proposes an improved class of efficient estimators using different transformations. When both the minimum and maximum values of the auxiliary variable are known and the ranks of the auxiliary variable are associated with the study variable, these estimators are particularly useful. Therefore, the precision of the estimators can be effectively improved through the utilization of these rankings. We examine the properties of the proposed class of estimators, including bias and mean squared error (MSE), using a first-order approximation through a stratified random sampling method. To determine the performances and validate the findings mathematically, a simulation study is carried out. Based on the results, the proposed class of estimators performs better in terms of the mean squared error (MSE) and percent relative efficiency (PRE) as compared to other estimators in all scenarios. Furthermore, in order to prove that the performances of the improved class of estimators are better than those of the existing estimators, three data sets are examined in the application section.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

New classes of difference cum-ratio-type exponential estimators for a finite population variance in stratified random sampling

Cited by 6 publications

References 21 publications

Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network

Research on the Optimization of TP2 Copper Tube Drawing Process Parameters Based on Particle Swarm Algorithm and Radial Basis Neural Network

New Techniques for Estimating Finite Population Variance Using Ranks of Auxiliary Variable in Two-Stage Sampling

Dual Transformation of Auxiliary Variables by Using Outliers in Stratified Random Sampling

Contact Info

Product

Resources

About