An Alternative Estimator for Estimating the Finite Population Mean Using Auxiliary Information in Sample Surveys

This paper suggests a class of estimators for estimating the finite population mean of the study variable using known population mean of the auxiliary variable . Asymptotic expressions of bias and variance of the suggested class of estimators have been obtained. Asymptotic optimum estimator (AOE) in the class is identified along with its variance formula. It has been shown that the proposed class of estimators is more efficient than usual unbiased, usual ratio, usual product, Bahl and Tuteja (1991), and Kadilar and Cingi (2003) estimators under some realistic conditions. An empirical study is carried out to judge the merits of suggested estimator over other competitors practically.

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Efficient Class of Estimators for Finite Population Mean using Auxiliary Information in Two-Occasion Successive Sampling

Journal of Modern Applied Statistical Methods ◽

10.22237/jmasm/1554138114 ◽

2019 ◽

Vol 17 (2) ◽

Author(s):

G. N. Singh ◽

Mohd Khalid

Keyword(s):

Linear Combination ◽

Finite Population ◽

Auxiliary Information ◽

Successive Sampling ◽

Sample Mean ◽

Population Means ◽

Population Mean ◽

Practical Applications ◽

Optimum Estimator ◽

Class Of Estimators

In the case of sampling on two occasions, a class of estimators is considered which uses information on the first occasion as well as the second occasion in order to estimate the population means on the current (second) occasion. The usefulness of auxiliary information in enhancing the efficiency of this estimation is examined through the class of proposed estimators. Some properties of the class of estimators and a strategy of optimum replacement are discussed. The proposed class of estimators were empirically compared with the sample mean estimator in the case of no matching. The established optimum estimator, which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion, was empirically compared with the proposed class of estimators. Mutual comparisons of the proposed estimator were carried out. Suitable recommendations are made to the survey statistician for practical applications.

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

Calcutta Statistical Association Bulletin ◽

10.1177/0008068319950307 ◽

1995 ◽

Vol 45 (3-4) ◽

pp. 203-218 ◽

Cited By ~ 3

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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Predictive Estimation of Finite Population Variance

Calcutta Statistical Association Bulletin ◽

10.1177/0008068319980310 ◽

1998 ◽

Vol 48 (3-4) ◽

pp. 229-236 ◽

Cited By ~ 6

Author(s):

R.S. Biradar ◽

H.P. Singh

Keyword(s):

Finite Population ◽

Auxiliary Information ◽

The Other ◽

Mean Square ◽

Population Variance ◽

Asymptotic Expressions ◽

Regression Estimators ◽

Predictive Approach ◽

Variance Formula ◽

Mean And Variance

Adopting predictive approach, estimators are proposed for population variance [Formula: see text] using different predictors for mean and variance of unobserved units in the population. Asymptotic expressions for bias and mean square error of these new estimators are obtained and compared with those of some known estimators of population variance. Predictive character of some known estimators is also examined . An empirical study demonstrated superiority over others of one of the proposed estimators, which uses regression estimators as predictors for mean and variance of unobserved units. Also, one of the other proposed estimators which does not utilize any auxiliary information has been found to be more efficient than the traditional unbiased estimator [Formula: see text].

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An Improved Family of Estimators of Finite Population Mean Using Information on an Auxiliary Variable in Sample Surveys

Journal of Applied Mathematics Statistics and Informatics ◽

10.1515/jamsi-2017-0011 ◽

2017 ◽

Vol 13 (2) ◽

pp. 77-108

Author(s):

H. P. Singh ◽

A. Yadav

Keyword(s):

Mean Squared Error ◽

Auxiliary Information ◽

Auxiliary Variable ◽

Sample Surveys ◽

Sample Mean ◽

Population Mean ◽

Squared Error ◽

Parameter Ratio ◽

Large Sample Approximation ◽

Class Of Estimators

Abstract In this paper we have suggested a family of estimators of the population mean using auxiliary information in sample surveys. The bias and mean squared error of the proposed class of estimators have been obtained under large sample approximation. We have derived the conditions for the parameters under which the proposed class of estimators has smaller mean squared error than the sample mean, ratio, product, regression estimator and the two parameter ratio-product-ratio estimators envisaged by Chami et al (2012). An empirical study is carried out to demonstrate the performance of the proposed class of estimators over other existing estimators.

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A Class of Estimator for Population Mean Under SRS

STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH ◽

10.52700/scir.v2i1.7 ◽

2020 ◽

Vol 2 (1) ◽

pp. 9-26

Author(s):

Syed Abdul Rehman ◽

Mohammad Asif

Keyword(s):

Mean Square Error ◽

Auxiliary Information ◽

Degree Of Approximation ◽

Mean Square ◽

Population Mean ◽

Class Of Estimators ◽

Efficiency Comparison

In this paper we propose a class of estimators for the estimation of finitepopulation mean using the auxiliary information when SRS scheme is used. Theexpressions for the Bias and mean square error (MSE) of the existing andsuggested class of estimators are derived up to first degree of approximation andthe efficiency comparison of suggested class of estimators is made with otherexisting estimators, using both theoretically and numerically based on realpopulation sets.

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