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 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.

scholarly journals Modified Ratio Estimators of Population Mean Based on Ranked Set Sampling

2019 ◽  
pp. 445-449 ◽  
Author(s):  
Zahid Khan ◽  
Muhammad Ismail
Keyword(s):  
Mean Square Error ◽  
Coefficient Of Variation ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Variation Coefficient ◽  
Mean Square ◽  
Population Mean ◽  
The Mean ◽  
Ratio Estimators ◽  
Coefficient Of Skewness

In this paper, we propose modified ratio estimators using some known values of coefficient of variation, coefficient of skewness and coefficient of kurtosis of auxiliary variable under ranked set sampling (RSS).  The mean square error (MSE) of the proposed ratio estimators under ranked set sampling is derived and compared with some existing ratio estimators under RSS. Through this comparison, we prove theoretically that MSC of proposed estimators is less than some existing ratio estimators in RSS under some conditions. The MSE of proposed estimators along with some existing estimator are also calculated numerically. We observe from numerical results that the suggested ratio estimators are more efficient than some existing ratio estimators under RSS.

scholarly journals Regression Estimator Using Double Ranked Set Sampling

2002 ◽  
Vol 7 (2) ◽  
pp. 311
Author(s):  
Hani M. Samawi ◽  
Eman M. Tawalbeh
Keyword(s):  
Correlation Coefficient ◽  
Real Data ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Regression Estimator ◽  
Primary Analysis ◽  
Data Set ◽  
Population Mean ◽  
The Mean

The performance of a regression estimator based on the double ranked set sample (DRSS) scheme, introduced by Al-Saleh and Al-Kadiri (2000), is investigated when the mean of the auxiliary variable X is unknown. Our primary analysis and simulation indicates that using the DRSS regression estimator for estimating the population mean substantially increases relative efficiency compared to using regression estimator based on simple random sampling (SRS) or ranked set sampling (RSS) (Yu and Lam, 1997) regression estimator.  Moreover, the regression estimator using DRSS is also more efficient than the naïve estimators of the population mean using SRS, RSS (when the correlation coefficient is at least 0.4) and DRSS for high correlation coefficient (at least 0.91.) The theory is illustrated using a real data set of trees.  

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 Robustness Test of the Two Stage K-L Estimator in Models with Multicollinear Regressors and Autocorrelated Error Term

2021 ◽  
pp. 22-33
Author(s):  
Zubair Mohammed Anono ◽  
Adenomon Monday Osagie
Keyword(s):  
Maximum Likelihood ◽  
Mean Square Error ◽  
Linear Regression Analysis ◽  
Real Life ◽  
Multiple Linear Regression Analysis ◽  
Least Square ◽  
Mean Square ◽  
Two Stage ◽  
Data Set ◽  
Real Life Data

In a classical multiple linear regression analysis, multicollinearity and autocorrelation are two main basic assumption violation problems. When multicollinearity exists, biased estimation techniques such as Maximum Likelihood, Restricted Maximum Likelihood and most recent the K-L estimator by Kibria and Lukman [1] are preferable to Ordinary Least Square. On the other hand, when autocorrelation exist in the data, robust estimators like Cochran Orcutt and Prais-Winsten [2] estimators are preferred. To handle these two problems jointly, the study combines the K-L with the Prais-Winsten’s two-stage estimator producing the Two-Stage K-L estimator proposed by Zubair & Adenomon [3]. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) criterion was used to compare the performance of the estimators. Application of the estimators to two (2) real life data set with multicollinearity and autocorrelation problems reveals that the Two Stage K-L estimator is generally the most efficient.

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.

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 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 Generalized Classes of Estimators for Population Mean, Ratio and Product Using Rank of Auxiliary Character Under Double Sampling the Non-Respondents

2020 ◽  
Vol 9 (2) ◽  
pp. 1-12
Author(s):  
Raghaw Raman Sinha
Keyword(s):  
Empirical Study ◽  
Comparative Study ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Double Sampling ◽  
Mean Square ◽  
Population Means ◽  
Population Mean ◽  
Auxiliary Character ◽  
Generalized Classes

AbstractIn the present study, generalized classes of estimators for estimating population mean, ratio and product of two population means using rank of auxiliary character in presence of non-response are proposed. The bias and mean square error of proposed classes of estimators are obtained and their performances examined. Specific conditions under which the members of proposed classes of estimators attain minimum mean square error are obtained. Comparative study of the proposed classes of estimators with the relevant estimators is carried out. An empirical study is given to justify the efficiency of the proposed classes of estimators.

Sign in / Sign up

Export Citation Format

Share Document