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 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.

Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling

2005 ◽  
Vol 10 (4) ◽  
pp. 333-342
Author(s):  
V. Chadyšas ◽  
D. Krapavickaitė
Keyword(s):  
Mean Square Error ◽  
Taylor Series ◽  
Random Sampling ◽  
Finite Population ◽  
Taylor Series Expansion ◽  
Simple Random Sampling ◽  
Population Parameter ◽  
Mean Square ◽  
Parameter Ratio ◽  
Using Data

Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.

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 Restructured class of estimators for population mean using an auxiliary variable under simple random sampling scheme

2020 ◽  
Vol 16 (1) ◽  
pp. 61-75
Author(s):  
S. Baghel ◽  
S. K. Yadav
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Sampling Scheme ◽  
Order Of Approximation ◽  
Study Variable ◽  
Population Mean ◽  
First Order ◽  
New Class ◽  
Class Of Estimators

AbstractThe present paper provides a remedy for improved estimation of population mean of a study variable, using the information related to an auxiliary variable in the situations under Simple Random Sampling Scheme. We suggest a new class of estimators of population mean and the Bias and MSE of the class are derived upto the first order of approximation. The least value of the MSE for the suggested class of estimators is also obtained for the optimum value of the characterizing scaler. The MSE has also been compared with the considered existing competing estimators both theoretically and empirically. The theoretical conditions for the increased efficiency of the proposed class, compared to the competing estimators, is verified using a natural population.

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 Restructured class of estimators for population mean using an auxiliary variable under simple random sampling scheme

2021 ◽  
Vol 17 (2) ◽  
pp. 75-90
Author(s):  
B. Prashanth ◽  
K. Nagendra Naik ◽  
R. Salestina M
Keyword(s):  
Characteristic Polynomial ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Sampling Scheme ◽  
Signed Graph ◽  
Population Mean ◽  
Class Of Estimators

Abstract With this article in mind, we have found some results using eigenvalues of graph with sign. It is intriguing to note that these results help us to find the determinant of Normalized Laplacian matrix of signed graph and their coe cients of characteristic polynomial using the number of vertices. Also we found bounds for the lowest value of eigenvalue.

scholarly journals Unbiased Estimation of Population Mean Using Lahiri-Midzuno-Sen Type Sampling Scheme

2020 ◽  
pp. 16-20
Author(s):  
Chandni Kumari ◽  
Ratan Kumar Thakur
Keyword(s):  
Random Sampling ◽  
Sampling Strategy ◽  
Simple Random Sampling ◽  
Sampling Scheme ◽  
Unbiased Estimation ◽  
Double Sampling ◽  
Order Of Approximation ◽  
Population Mean ◽  
First Order ◽  
Class Of Estimators

This paper considers the problem of estimating the population mean under double sampling. We have suggested the generalized class of estimators under Lahiri (1951) to Midzuno (1952) and Sen (1952) type sampling scheme and its properties are studied up to the first order of approximation. Further, we compare the proposed sampling strategy with some conventional estimators under the simple random sampling without replacement. On the basis of suitable range information, we give some concluding remarks related to propose sampling strategy. An empirical study is given in support of the present study.

scholarly journals Poisson Regression-Based Mean Estimator

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Usman Shahzad ◽  
Shabnam Shahzadi ◽  
Noureen Afshan ◽  
Nadia H. Al-Noor ◽  
David Anekeya Alilah ◽  
...  
Keyword(s):  
Regression Model ◽  
Mean Square Error ◽  
Random Sampling ◽  
Poisson Regression ◽  
Simple Random Sampling ◽  
Mean Square ◽  
Poisson Regression Model ◽  
The Mean ◽  
Ratio Estimators

The most frequent method for modeling count responses in numerous investigations is the Poisson regression model. Under simple random sampling, this paper offers utilizing Poisson regression-based mean estimator and discovers its associated formula of the mean square error (MSE). The MSE of the proposed estimator is compared to the MSE of traditional ratio estimators in theory. As a result of these evaluations, the proposed estimator has been proven to be more efficient than traditional estimators. Furthermore, the practical results corroborated the theoretical findings.

scholarly journals ESTIMATION OF POPULATION VARIANCE IN LOG – PRODUCT TYPE ESTIMATORS UNDER DOUBLE SAMPLING SCHEME

2019 ◽  
pp. 11-20
Author(s):  
Prabhakar Mishra ◽  
Rajesh Singh ◽  
Supriya Khare
Keyword(s):  
Random Sampling ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Product Type ◽  
Sampling Scheme ◽  
Double Sampling ◽  
Population Variance ◽  
Population Mean ◽  
Ancillary Information

It is experienced that auxiliary information when suitably incorporated yields more efficient and precise estimates. Mishra et al. (2017) have introduced a log type estimator for estimating unknown population mean using ancillary information in simple random sampling. Here we propose an improved log-product type estimator for population variance under double sampling. Properties of the estimators are studied both mathematically and numerically.  

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