scholarly journals Finite Population Mean Estimation through a Two-Parameter Ratio Estimator Using Auxiliary Information in Presence of Non-Response

2016 ◽  
Vol 12 (2) ◽  
pp. 5-39 ◽  
Author(s):  
S. K. Pal ◽  
H. P. Singh
Keyword(s):  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Human Populations ◽  
Ratio Estimator ◽  
Population Mean ◽  
Parameter Ratio ◽  
Mean Estimation ◽  
Large Sample Approximation ◽  
Respondent Group ◽  
Sample Approximation

Abstract In surveys covering human populations it is observed that information in most cases are not obtained at the first attempt even after some callbacks. Such problems come under the category of non-response. Surveys suffer with non-response in various ways. It depends on the nature of required information, either surveys is concerned with general or sensitive issues of a society. Hansen and Hurwitz (1946) have considered the problem of non-response while estimating the population mean by taking a subsample from the non-respondent group with the help of extra efforts and an estimator was suggested by combining the information available from the response and nonresponse groups. We also mention that in survey sampling auxiliary information is commonly used to improve the performance of an estimator of a quantity of interest. For estimating the population mean using auxiliary information in presence of non-response has been discussed by various authors. In this paper, we have developed estimators for estimating the population mean of the variable under interest when there is non-response error in the study as well as in the auxiliary variable. We have studied properties of the suggested estimators under large sample approximation. Comparison of the suggested estimators with usual unbiased estimator reported by Hansen and Hurwitz (1946) and the ratio estimator due to Rao (1986) have been made. The results obtained are illustrated with aid of an empirical study.

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

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.

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 Estimation of current population mean using two-occasion successive sampling with one auxiliary variable

2018 ◽  
Vol 18 (2) ◽  
pp. 74-77
Author(s):  
R. Zoramthanga
Keyword(s):  
Mean Square Error ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Ratio Estimator ◽  
Mean Square ◽  
Current Estimate ◽  
Successive Sampling ◽  
Population Mean ◽  
Female Workers ◽  
Chain Type

In this study, two-occasion successive sampling for ratio-to-regression estimator was used to determine the current estimate of the population mean using only the matched part and one auxiliary variable, which is available on both the occasions. The data used were based on the total number of female workers in villages in Mizoram with the total number of literate female in villages in Mizoram as an auxiliary variables. The data were gotten from Census of India 2001 and 2011. The optimum mean square error of the combined ratio-to-regression and ratio estimator has been compared with (i) the optimum mean square error of the chain-type ratio estimator (ii) mean per unit estimator and (iii) combined estimator when no auxiliary information is used at any occasion. This result showed that the combined ratio-to-regression and ratio estimator is more efficient than the other three existing estimators.

scholarly journals On Improving Ratio/Product Estimator by Ratio/Product-cum-Mean-per-Unit Estimator Targeting More Efficient Use of Auxiliary Information

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Angela Shirley ◽  
Ashok Sahai ◽  
Isaac Dialsingh
Keyword(s):  
Auxiliary Information ◽  
Simple Random Sample ◽  
Sample Mean ◽  
Parameter Ratio ◽  
Large Sample Approximation ◽  
Product Estimator ◽  
Sample Approximation ◽  
The Mean ◽  
Classical Linear Regression ◽  
Linear Regression Estimator

To achieve a more efficient use of auxiliary information we propose single-parameter ratio/product-cum-mean-per-unit estimators for a finite population mean in a simple random sample without replacement when the magnitude of the correlation coefficient is not very high (less than or equal to 0.7). The first order large sample approximation to the bias and the mean square error of our proposed estimators are obtained. We use simulation to compare our estimators with the well-known sample mean, ratio, and product estimators, as well as the classical linear regression estimator for efficient use of auxiliary information. The results are conforming to our motivating aim behind our proposition.

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 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.

scholarly journals Estimation of finite population mean using dual auxiliary variable for non-response using simple random sampling

2021 ◽  
Vol 7 (3) ◽  
pp. 4592-4613
Author(s):  
Sohaib Ahmad ◽  
◽  
Sardar Hussain ◽  
Muhammad Aamir ◽  
Faridoon Khan ◽  
...  
Keyword(s):  
Random Sampling ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Sample Mean ◽  
Population Mean ◽  
New Family ◽  
Variable Bias ◽  
Mean Square Errors ◽  
The Given

<abstract><p>This paper addresses the issue of estimating the population mean for non-response using simple random sampling. A new family of estimators is proposed for estimating the population mean with auxiliary information on the sample mean and the rank of the auxiliary variable. Bias and mean square errors of existing and proposed estimators are obtained using the first order of measurement. Theoretical comparisons are made of the performance of the proposed and existing estimators. We show that the proposed family of estimators is more efficient than existing estimators in the literature under the given constraints using these theoretical comparisons.</p></abstract>

scholarly journals Ameliorated Ratio Estimator of Population Mean Using New Linear Combination

2020 ◽  
pp. 76-79
Author(s):  
T. A. Raja ◽  
S. Maqbool
Keyword(s):  
Linear Combination ◽  
Mean Square Error ◽  
Auxiliary Variable ◽  
Ratio Estimator ◽  
Mean Square ◽  
Population Mean ◽  
Main Variable ◽  
Better Than

We propose a new modified ratio estimator of population mean of the main variable using the linear combination of known values of Co-efficient of Kurtosis and Tri-Mean of the auxiliary variable. Mean Square Error (MSE) and bias of the proposed estimator is calculated and compared with the existing estimator. The comparison is demonstrated numerically which shows that the proposed estimator performs better than the existing estimators.

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 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.

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