scholarly journals Some Improved Estimators of Population Mean using Two-Phase Sampling Scheme in the Presence of Non-Response

2021 ◽  
pp. 911-919
Author(s):  
Manoj Kumar Chaudhary ◽  
Amit Kumar ◽  
Gautam K. Vishwakarma
Keyword(s):  
Auxiliary Variable ◽  
Study Variable ◽  
Two Phase ◽  
Population Mean ◽  
Usual Estimator ◽  
Phase Sampling ◽  
Improved Estimators ◽  
Mean Square Errors ◽  
Theoretical Results ◽  
Two Phase Sampling

In the present paper, we have proposed some improved estimators of the population mean utilizing the information on two auxiliary variables adopting the idea of two-phase sampling under non-response. In order to propose the estimators, we have assumed that the study variable and first auxiliary variable suffer from non-response while the second (additional) auxiliary variable is free from non-response. We have derived the expressions for biases and mean square errors of the proposed estimators and compared them with that of usual estimator and some well known existing estimators of the population mean. The theoretical results have also been illustrated with some empirical data.

scholarly journals Modified Regression Type Estimators in the Presence of Non-Response in Two Phase sampling

2018 ◽  
Vol 6 (07) ◽  
Author(s):  
John Kung’u Wanjiru ◽  
Grace Chumba
Keyword(s):  
Single Phase ◽  
Complete Response ◽  
Auxiliary Variable ◽  
Degree Of Approximation ◽  
Sample Survey ◽  
Sampling Scheme ◽  
Two Phase ◽  
Population Mean ◽  
Phase Sampling ◽  
Two Phase Sampling

It is a common experience in sample survey that data cannot always be collected for all units selected in the sample at the first attempt and even after some call-backs. An estimate obtained from such incomplete data may be misleading because of the non-response in the data. In addition, the population mean of the auxiliary variable from the previous census may not be available. In this paper, Modified regression type estimators proposed by Tum et al. (2014) in single phase sampling, assuming complete response, have been proposed to estimate the population mean of the study variable in the presence of non-response under two phase sampling scheme. The expression of mean squared errors (MSE) based on the proposed estimators have been derived under two phase sampling to the first degree of approximation. A comparison of the proposed estimators with the usual unbiased estimator and existing estimators under two phase sampling scheme have been carried out. The proposed Modified regression type estimators have been found to be the most efficient compared to the existing estimators and they are recommended for use in practice.

A New Cost Effective Estimator in the Presence of Non-response for Two-phase Sampling

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Muhammad Ismail ◽  
Muhammad Hanif ◽  
Muhammad Qaiser Shahbaz
Keyword(s):  
Cost Effective ◽  
Auxiliary Variable ◽  
Two Phase ◽  
Population Mean ◽  
Phase Sampling ◽  
The Cost ◽  
Two Phase Sampling

In In this paper we have proposed a new estimator of population mean in the presence of non–response using information of a single auxiliary variable. We have obtained survey cost for the fixed variance of the proposed estimator and compared it with the cost obtained by Tabasum and Khan (2004) and Hansen Hurwitz (1946). After the comparison we saw that the cost of our proposed estimator is lesser than Tabasum and Khan (2004) and Hansen Hurwitz (1946) estimators.

A Class of Estimators for a Finite Population Mean Based on Multivariate Information and General two Phase Sampling

1995 ◽  
Vol 45 (3-4) ◽  
pp. 203-218 ◽  
Author(s):  
T. P. Tripathi ◽  
M. S. Ahmed
Keyword(s):  
Finite Population ◽  
Population Data ◽  
Two Phase ◽  
Population Means ◽  
Population Mean ◽  
Phase Sampling ◽  
Optimum Estimator ◽  
Class Of Estimators ◽  
Relative Gains ◽  
Two Phase Sampling

A class of estimators for a finite population mean is presented for the situations where population means of some auxiliary variables are known while those of others are unknown. The results for general two phase sampling are indicated while the detailed discussion is made for the case when SRSWOR is used at both the phases. While several known estimators belong to the proposed clas~ some new estimators are identified as well. The optimum estimator in the proposed class is found to be better than the so-called chain ratio and regression estimators discu ssed by Chand (1975). Kiregyera (1984) and Mukerjee et al. (1987). The relative gains in efficiency of tho proposed optimum estimator over the others are obtained for a natural population data and found to be quite appreciable.

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