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 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.

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 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.

AN EFFICIENT CLASS OF PRODUCT ESTIMATORS USING MEASURES OF DISPERSIONS

2021 ◽  
Vol 21 (2) ◽  
pp. 347-354
Author(s):  
MUHAMMAD IJAZ ◽  
TOLGA ZAMAN ◽  
BUSHRA HAIDER ◽  
SYED MUHAMMAD ASIM
Keyword(s):  
Standard Deviation ◽  
Mean Square Error ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Mean Deviation ◽  
Mean Square ◽  
Sampling Without Replacement ◽  
Population Mean ◽  
Class Of Estimators ◽  
Secondary Information

The study suggests a class of product estimators for estimating the population mean of variable under investigation in simple random sampling without replacement (SRSWOR) scheme when secondary information on standard deviation, mean deviation, and quartile deviation is available. The expression for Bias and Mean Square Error (MSE) has been derived. A comparison is made both theoretically and numerically with other existing product estimators. It is concluded that compared to other product type estimators, suggested class of estimators estimate the population mean more efficiently.

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.

A class of estimators of the population mean in survey sampling using auxiliary information

Biometrika ◽  
1981 ◽  
Vol 68 (1) ◽  
pp. 341-343 ◽  
Author(s):  
SURENDRA K. SRIVASTAVA ◽  
HARBANS SINGH JHAJJ
Keyword(s):  
Auxiliary Information ◽  
Survey Sampling ◽  
Population Mean ◽  
Class Of Estimators

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.

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