- Home
-
Board
- Editorial Board
- Apply For Reviewer
-
Research Publishing Standards
- Guidelines
- Peer Review Policy
- Short Guide to New Editors
- Retraction Guidelines
- Research Institute
- Recommendations for the Board of Directors
- Editor Research Audit and Service Assessments
- Guidelines for Journals Publishers
- Code of Conduct for Journal Publishers
- Commandment in Social Media
- Suggestions
- Authorship Disputes
- Rights and Responsibilities
- Editors Home
- Visibility
- Information
- Apply for Editorial
- About
- Call for Paper
- Research Community
-
Journals
- Computer Science
-
Medical Research
- A: Neurology & Nervous System
- B: Pharma, Drug Discovery, Toxicology & Medicine
- C: Microbiology & Pathology
- D: Radiology, Diagnostic Imaging and Instrumentation
- E: Gynecology & Obstetrics
- F: Diseasescancer, ophthalmology & pediatric
- G: Veterinary Science & Veterinary Medicine
- H: Orthopedic & Musculoskeletal System
- I: Surgeries & Cardiovascular System
- J: Dentistry & Otolaryngology
- K: Interdisciplinary
- L:Nutrition and Food Science
- Engineering
- Science Frontier Research
- Management
-
Human-Social Science
- A: Arts & Humanities psychology, public administration, library sciences, sports, arts, media, music
- B: Geography, Environmental Science & Disaster Management
- C: Sociology & Culture
- D: History, Archaeology & Anthropology
- E: Economics
- F: Political Science
- G: Linguistics & Education
- H: Interdisciplinary
-
Fellow
-
Support
Account
×
An Efficient Class of Ratio-Cum-Dual to Product Estimator of Finite Population Mean in Sample Surveys
Dr. Sanjib Choudhury, Bhupendra Kumar Singh
Volume 12 Issue 12
Global Journal of Science Frontier Researc
Volume 12 Issue 12
Global Journal of Science Frontier Researc
We consider a class of ratio-cum-dual to product estimator for estimating a finite population mean of the study variate. The bias and mean square error of the proposed estimator have been obtained. The asymptotically optimum estimator (AOE) in this class has also been identified along with its approximate bias and mean square error. Theoretical and empirical studies have been done to demonstrate the superiority of the proposed estimator over the other estimators.
