scholarly journals A family of estimators of population mean using multi-auxiliary variate and post-stratification

2010 ◽  
Vol 15 (2) ◽  
pp. 233-253 ◽  
Author(s):  
Gajendra K. Vishwakarma ◽  
Housila P. Singh ◽  
Sarjinder Singh
Keyword(s):  
Empirical Study ◽  
Stratified Sampling ◽  
Large Sample ◽  
Population Mean ◽  
Auxiliary Variate ◽  
Large Sample Approximation ◽  
Sample Approximation ◽  
Optimum Estimator ◽  
Class Of Estimators ◽  
Approximate Variance

This paper suggests a family of estimators of population mean using multiauxiliary variate based on post-stratified sampling and its properties are studied under large sample approximation. Asymptotically optimum estimator in the class is identified alongwith its approximate variance formulae. The proposed class of estimators is also compared with corresponding unstratified class of estimators based on estimated optimum value. At the end, an empirical study has been carried out to support the proposed methodology.

scholarly journals Two- Phase Stratified Sampling Estimator for Population Mean in The Presence of Nonresponse Using Single Auxiliary Variable

2020 ◽  
Vol 8 (2) ◽  
pp. 49-56
Author(s):  
Akan Anieting
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Variable ◽  
Stratified Sampling ◽  
Second Phase ◽  
Two Phase ◽  
Population Mean ◽  
Squared Error ◽  
Large Sample Approximation ◽  
Sample Approximation ◽  
Phase Sample

In this article, a new estimator for population mean in two-phase stratified sampling in the presence of nonresponse using single auxiliary variable has been proposed. The bias and Mean Squared Error (MSE) of the proposed estimator has been given using large sample approximation. The empirical study shows that the MSE of the proposed estimator is more efficient than existing estimators. The optimum values of first and second phase sample have been determined.

scholarly journals A New Exponential Approach for Reducing the Mean Squared Errors of the Estimators of Population Mean Using Conventional and Non-Conventional Location Parameters

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Housila P. Singh ◽  
Anita Yadav
Keyword(s):  
Large Sample ◽  
Population Mean ◽  
Mean Squared Errors ◽  
Large Sample Approximation ◽  
Location Parameters ◽  
Sample Approximation ◽  
The Mean

Classes of ratio-type estimators t (say) and ratio-type exponential estimators te (say) of the population mean are proposed, and their biases and mean squared errors under large sample approximation are presented. It is the class of ratio-type exponential estimators te provides estimators more efficient than the ratio-type estimators.

scholarly journals On Using the Conventional and Nonconventional Measures of the Auxiliary Variable for Mean Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Javid Shabbir ◽  
Sat Gupta ◽  
Ronald Onyango
Keyword(s):  
Survey Research ◽  
Finite Population ◽  
Auxiliary Variable ◽  
Large Sample ◽  
New Class ◽  
Numerical Studies ◽  
Mean Estimation ◽  
Large Sample Approximation ◽  
Sample Approximation ◽  
Class Of Estimators

In this paper, we propose an improved new class of exponential-ratio-type estimators for estimating the finite population mean using the conventional and the nonconventional measures of the auxiliary variable. Expressions for the bias and MSE are obtained under large sample approximation. Both simulation and numerical studies are conducted to validate the theoretical findings. Use of the conventional and the nonconventional measures of the auxiliary variable is very common in survey research, but we observe that this does not add much value in many of the estimators except for our proposed class of 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.

scholarly journals Efficient Estimator for Population Mean in Stratified Double Sampling in the Presence of Nonresponse Using One Auxiliary Variable

2021 ◽  
Vol 4 (2) ◽  
pp. 41-51
Author(s):  
A.E. Anieting ◽  
E. I. Enang ◽  
C. E. Onwukwe
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Variable ◽  
Double Sampling ◽  
Population Mean ◽  
Squared Error ◽  
Large Sample Approximation ◽  
Sample Approximation ◽  
Second Phases ◽  
Optimal Values

A modified form of the population mean estimator suggested by Anieting and Enang (2020) in stratified double sampling in the presence of nonresponse using a single auxiliary variable has been proposed. The Mean Squared Error (MSE) and the bias of the proposed estimator have been given using large sample approximation. The empirical study shows that the MSE of the suggested estimator is more efficient than all other existing estimators in the same scheme. Determination of the optimal values of the first and second phases samples has also been done

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

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.

scholarly journals An Improved Class of Chain Ratio-Product Type Estimators in Two-Phase Sampling Using Two Auxiliary Variables

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Gajendra K. Vishwakarma ◽  
Manish Kumar
Keyword(s):  
Regression Line ◽  
Auxiliary Variables ◽  
Study Variable ◽  
Two Phase ◽  
Large Sample Approximation ◽  
Phase Sampling ◽  
Sample Approximation ◽  
Class Of Estimators ◽  
Pass Through ◽  
Two Phase Sampling

This paper presents a technique for estimating finite population mean of the study variable in the presence of two auxiliary variables using two-phase sampling scheme when the regression line does not pass through the neighborhood of the origin. The properties of the proposed class of estimators are studied under large sample approximation. In addition, bias and efficiency comparisons are carried out to study the performances of the proposed class of estimators over the existing estimators. It has also been shown that the proposed technique has greater applicability in survey research. An empirical study is carried out to demonstrate the performance of the proposed estimators.

scholarly journals A Class of Estimators for Finite Population Mean in Double Sampling under Nonresponse Using Fractional Raw Moments

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Manzoor Khan ◽  
Javid Shabbir ◽  
Zawar Hussain ◽  
Bander Al-Zahrani
Keyword(s):  
Empirical Study ◽  
Mean Square Error ◽  
Finite Population ◽  
Degree Of Approximation ◽  
Double Sampling ◽  
Mean Square ◽  
Population Mean ◽  
Class Of Estimators ◽  
Better Than

This paper presents new classes of estimators in estimating the finite population mean under double sampling in the presence of nonresponse when using information on fractional raw moments. The expressions for mean square error of the proposed classes of estimators are derived up to the first degree of approximation. It is shown that a proposed class of estimators performs better than the usual mean estimator, ratio type estimators, and Singh and Kumar (2009) estimator. An empirical study is carried out to demonstrate the performance of a proposed class of estimators.

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