scholarly journals A Simulation-Based Study for Progressive Estimation of Population Mean through Traditional and Nontraditional Measures in Stratified Random Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Maria Javed ◽  
Muhammad Irfan ◽  
Sajjad Haider Bhatti ◽  
Ronald Onyango
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Auxiliary Information ◽  
Stratified Random Sampling ◽  
Mean Square ◽  
Population Mean ◽  
New Family ◽  
Simulation Based ◽  
Better Than

This study suggests a new optimal family of exponential-type estimators for estimating population mean in stratified random sampling. These estimators are based on the traditional and nontraditional measures of auxiliary information. Expressions for the bias, mean square error, and minimum mean square error of the proposed estimators are derived up to first order of approximation. It is observed that proposed estimators perform better than the traditional estimators (unbiased, combined ratio, and combined regression) and other recent estimators. A real dataset is used to highlight the applicability of proposed estimators. In addition, a simulation study is carried out to assess the performance of new family as compared to other estimators.

A MODIFIED RATIO TYPE ESTIMATOR OF FINITE POPULATION MEAN UNDER STRATIFIED RANDOM SAMPLING SCHEME

2021 ◽  
pp. 58-60
Author(s):  
Naziru Fadisanku Haruna ◽  
Ran Vijay Kumar Singh ◽  
Samsudeen Dahiru
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Real Life ◽  
Population Data ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Data Set ◽  
Population Mean ◽  
The Mean

In This paper a modied ratio-type estimator for nite population mean under stratied random sampling using single auxiliary variable has been proposed. The expression for mean square error and bias of the proposed estimator are derived up to the rst order of approximation. The expression for minimum mean square error of proposed estimator is also obtained. The mean square error the proposed estimator is compared with other existing estimators theoretically and condition are obtained under which proposed estimator performed better. A real life population data set has been considered to compare the efciency of the proposed estimator numerically.

scholarly journals On the Estimation of Population Mean Under Systematic Sampling Using Auxiliary Attributes

2016 ◽  
Vol 1 (1-2) ◽  
pp. 21-25 ◽  
Author(s):  
Usman Shahzad
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Sampling Scheme ◽  
Systematic Sampling ◽  
Mean Square ◽  
Numerical Illustration ◽  
Systematic Random Sampling ◽  
Population Mean ◽  
New Family

Naik and Gupta (1996), Singh et al. (2007) and Abd-Elfattah et al. (2010) introduced some estimators for estimating population mean using available auxiliary attributes under simple random sampling scheme. We adapt these estimators under systematic random sampling scheme using available auxiliary attributes. Further, a new family of estimators is proposed for the estimation of population mean under systematic random sampling scheme. The properties such as bias and mean square error of the proposed estimators are derived. From numerical illustration it is shown that proposed estimators are more efficient than the reviewed ones.

scholarly journals Dual Use of Auxiliary Information for Estimating the Finite Population Mean under the Stratified Random Sampling Scheme

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sohaib Ahmad ◽  
Sardar Hussain ◽  
Muhammad Aamir ◽  
Uzma Yasmeen ◽  
Javid Shabbir ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Simulation Study ◽  
Random Sampling ◽  
Finite Population ◽  
Stratified Random Sampling ◽  
Data Sets ◽  
Mean Square ◽  
Sample Mean ◽  
Population Mean ◽  
Mean Square Errors

In this paper, we proposed an improved family of estimators for finite population mean under stratified random sampling, which needed a helping variable on the sample mean and rank of the auxiliary variable. The expression of the bias and mean square error of the proposed and existing estimators are computed up to the first-order approximation. The estimators proposed in different situations were investigated and provided a minimum mean square error relative to all other estimators considered. Four actual data sets and simulation studies are carried out to observe the performance of the estimators. For simulation study, R software is used. The mean square errors of all four data sets are minimum and percent relative efficiencies are more than a hundred percent higher than the other existing estimators, which indicated the importance of the newly proposed family of estimators. From the simulation study, it is concluded that the suggested family of estimators achieved better results. We demonstrate theoretically and numerically that the proposed estimator produces efficient results compared to all other contend estimators in entire situations. Overall, we conclude that the performance of the family of suggested estimators is better than all existing estimators.

scholarly journals A Class of Estimator for Population Mean Under SRS

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.

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 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 Families of Estimators for Estimating Mean Using Information of Auxiliary Variate Under Response and Non-Response

2020 ◽  
Author(s):  
R. R Sinha ◽  
Bharti 
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Real Data ◽  
Fixed Cost ◽  
Data Sets ◽  
Mean Square ◽  
Unknown Parameters ◽  
Two Phase ◽  
Population Mean ◽  
Auxiliary Variate

This research article is concerned with the efficiency improvement of estimators for finite population mean under complete and incomplete information rising as a result of non-response. Different families of estimators for estimating the mean of study variate via known population mean, proportion and rank of auxiliary variate under different situations are proposed along with their bias and mean square error (MSE). Optimum conditions are suggested to attain minimum mean square error of proposed families of estimators. Further the problem is extended for the situation of unknown parameters of auxiliary variate and two phase sampling families of estimators are suggested along with their properties under fixed cost and precision. Employing real data sets, theoretical and empirical comparisons are executed to explain the efficiency of the proposed families of estimators.

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