Generalized multi-phase regression-type estimators under the effect of measuemnent error to estimate the population variance

被引：0

作者：

Masood, Saadia ^{[1
]}

Shabbir, Javid ^{[1
]}

机构：

[1] Quaid I Azam Univ, Dept Stat, Islamabad, Pakistan

来源：

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS | 2016年 / 45卷 / 04期

关键词：

Multi-phase sampling; measurement error; mean square error; efficiency; AUXILIARY INFORMATION;

D O I：

暂无

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this article, we suggest some regression-type estimators for the estimation of finite population variance using multi-variate auxiliary information under multi-phase sampling schemes when measurement error (ME) contaminates the study variable. An empirical study is also carried out to judge the merits of proposed estimators.

引用

页码：1297 / 1306

页数：10

共 6 条

[1] Asymptotic properties of a generalized regression-type predictor of a finite population variance in probability sampling
Shah, DN
Patel, PA
[J]. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 1996, 24 (03): : 373 - 384
[2] Generalized regression cum ratio estimators of population variance in two phase sampling
Kaleem, Sana
Khan, Hina
Aslam, Muhammad
[J]. JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS, 2019,
[3] Regression Type Estimators of Finite Population Variance Under Multiphase Sampling
Pradhan, B. K.
[J]. BULLETIN OF THE MALAYSIAN MATHEMATICAL SCIENCES SOCIETY, 2010, 33 (02) : 345 - 353
[4] GENERALIZED MULTIVARIATE REGRESSION ESTIMATORS FOR MULTI-PHASE SAMPLING USING MULTI-AUXILIARY VARIABLES
Ahmad, Zahoor
Hussin, Abdul Ghapor
Hanif, Muhammad
[J]. PAKISTAN JOURNAL OF STATISTICS, 2010, 26 (04): : 569 - 583
[5] Generalized Exponential Estimators for Population Variance Under Two-Phase Sampling
Sanaullah A.
Hanif M.
Asghar A.
[J]. International Journal of Applied and Computational Mathematics, 2016, 2 (1) : 75 - 84
[6] A Class of Exponential Regression Type Estimators for Population Variance in Two-Phase Sampling
A. Chatterjee
G. N. Singh
A. Bandyopadhyay
[J]. Journal of Statistical Theory and Applications, 2017, 16 (4): : 565 - 575

← 1 →