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

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.

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Families of estimators for ratio and product of study characters using mean and proportion of auxiliary character in presence of non-response

International Journal of Accounting and Economics Studies ◽

10.14419/ijaes.v4i2.6376 ◽

2016 ◽

Vol 4 (2) ◽

pp. 142

Author(s):

Raghaw Sinha

Keyword(s):

Mean Square Error ◽

Minimum Mean Square Error ◽

Real Data ◽

Degree Of Approximation ◽

Data Sets ◽

Mean Square ◽

Population Means ◽

Auxiliary Character

In this paper, families of estimators for ratio and product of two population means are suggested using proportion and mean of auxiliary character in presence of non-response. The bias and mean square error (MSE) of the proposed families of estimators are obtained up to the first degree of approximation under two different cases. The specified conditions under which the members of proposed families of estimators attain minimum mean square error have been obtained. Theoretical and empirical comparisons based on real data sets are made to show that the suggested families of estimators are more efficient than the relevant estimators such as usual conventional estimator, (Khare & Sinha 2012 a) estimators and (Sinha 2014) estimators.

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A Class of Estimators for Finite Population Mean in Double Sampling under Nonresponse Using Fractional Raw Moments

Journal of Applied Mathematics ◽

10.1155/2014/282065 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 1

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.

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

10.36106/4917049 ◽

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.

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

Journal of Reliability and Statistical Studies ◽

10.13052/jrss0974-8024.1312 ◽

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

Journal of Mathematics ◽

10.1155/2021/9038126 ◽

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.

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Cancelable Biometric System for face Based on linear minimum mean square error Model.

Menoufia Journal of Electronic Engineering Research ◽

10.21608/mjeer.2019.76994 ◽

2019 ◽

Vol 28 (1) ◽

pp. 145-152

Author(s):

Abd El-aziz Ebrahim Hsaneen ◽

EL-Sayed M. El-Rabaei ◽

Moawad I. Dessouky ◽

Ghada El-bamby ◽

Fathi E. Abd El-Samie ◽

...

Keyword(s):

Mean Square Error ◽

Minimum Mean Square Error ◽

Error Model ◽

Mean Square ◽

Biometric System

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Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology

Applied Sciences ◽

10.3390/app11093763 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3763

Author(s):

Yunlong Zou ◽

Jinyu Zhao ◽

Yuanhao Wu ◽

Bin Wang

Keyword(s):

Mean Square Error ◽

Selection Process ◽

Minimum Mean Square Error ◽

Correlation Coefficients ◽

False Alarms ◽

Mean Square ◽

Segmentation Method ◽

Space Objects ◽

High Earth Orbits ◽

Star Images

Space object recognition in high Earth orbits (between 2000 km and 36,000 km) is affected by moonlight and clouds, resulting in some bright or saturated image areas and uneven image backgrounds. It is difficult to separate dim objects from complex backgrounds with gray thresholding methods alone. In this paper, we present a segmentation method of star images with complex backgrounds based on correlation between space objects and one-dimensional (1D) Gaussian morphology, and the focus is shifted from gray thresholding to correlation thresholding. We build 1D Gaussian functions with five consecutive column data of an image as a group based on minimum mean square error rules, and the correlation coefficients between the column data and functions are used to extract objects and stars. Then, lateral correlation is repeated around the identified objects and stars to ensure their complete outlines, and false alarms are removed by setting two values, the standard deviation and the ratio of mean square error and variance. We analyze the selection process of each thresholding, and experimental results demonstrate that our proposed correlation segmentation method has obvious advantages in complex backgrounds, which is attractive for object detection and tracking on a cloudy and bright moonlit night.

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