Ameliorate estimation of mean using skewness and kurtosis of auxiliary character. Issue 4 (19th May 2022)
- Record Type:
- Journal Article
- Title:
- Ameliorate estimation of mean using skewness and kurtosis of auxiliary character. Issue 4 (19th May 2022)
- Main Title:
- Ameliorate estimation of mean using skewness and kurtosis of auxiliary character
- Authors:
- Sinha, R. R.
Bharti, - Abstract:
- Abstract: In this research article, improved regressed exponential estimators for estimating the finite population mean have been suggested using the coefficient of skewness and kurtosis of an auxiliary character. Other known parameters and constants like median, quartile deviation, Gini's mean difference, Downton's method and probability weighted moments of auxiliary character are utilized to improve the efficiency of the suggested estimators. The bias (Bs) and mean square error (Mse) of proposed estimators are obtained up to the first degree of approximation under large sample approach. The values of unknown constants involved in proposed estimators are obtained to minimize the mean square errors. Theoretical and empirical discussions reveal that the proposed estimators are more efficient than the traditional and all the recent relevant estimators.
- Is Part Of:
- Journal of statistics & management systems. Volume 25:Issue 4(2022)
- Journal:
- Journal of statistics & management systems
- Issue:
- Volume 25:Issue 4(2022)
- Issue Display:
- Volume 25, Issue 4 (2022)
- Year:
- 2022
- Volume:
- 25
- Issue:
- 4
- Issue Sort Value:
- 2022-0025-0004-0000
- Page Start:
- 927
- Page End:
- 944
- Publication Date:
- 2022-05-19
- Subjects:
- 62D05
Mean -- Bias -- Mean square error -- Skewness -- Kurtosis
Statistics -- Periodicals
Mathematical models -- Periodicals
Mathematical models
Statistics
Periodicals
519.5 - Journal URLs:
- http://www.tandfonline.com/loi/tsms20 ↗
- DOI:
- 10.1080/09720510.2021.1966956 ↗
- Languages:
- English
- ISSNs:
- 0972-0510
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 22973.xml