scholarly journals Modified Class of Estimator for Finite Population Mean Under Two-Phase Sampling Using Regression Estimation Approach

2021 ◽  
pp. 52-64
Author(s):  
A. Y. Erinola ◽  
R. V. K. Singh ◽  
A. Audu ◽  
T. James
Keyword(s):  
Order Approximation ◽  
Simple Random Sampling ◽  
Study Variable ◽  
Two Phase ◽  
Population Mean ◽  
The Mean ◽  
First Order Approximation ◽  
Mean Square Errors ◽  
Percentage Relative Efficiency ◽  
Taylor’S Series

This study proposed modified a class of estimator in simple random sampling for the estimation of population mean of the study variable using as axillary information. The biases and MSE of suggested estimators were derived up to the first order approximation using Taylor’s series expansion approach. Theoretically, the suggested estimators were compared with the existing estimators in the literature. The mean square errors (MSE) and percentage relative efficiency (PRE) of proposed estimators and that of some existing estimators were computed numerically and the results revealed that the members of the proposed class of estimator were more efficient compared to their counterparts and can produce better estimates than other estimators considered in the study.

scholarly journals Efficient Generalized Ratio-Product Type Estimators for Finite Population Mean with Ranked Set Sampling

2016 ◽  
Vol 42 (3) ◽  
pp. 137-148 ◽  
Author(s):  
V.L. Mandowara ◽  
Nitu Mehta
Keyword(s):  
Mean Squared Error ◽  
Order Approximation ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Power Transformation ◽  
Numerical Illustration ◽  
Population Mean ◽  
Squared Error ◽  
The Mean ◽  
First Order Approximation

In this paper we suggest two modified estimators of the population mean using the power transformation based on ranked set sampling (RSS). The first order approximation of the bias and of the mean squared error of the proposed estimators are obtained. A generalized version of the suggested estimators by applying the power transformation is also presented. Theoretically, it is shown that these suggested estimators are more efficient than the estimators in simple random sampling (SRS). A numerical illustration is also carried out to demonstrate the merits of the proposed estimators using RSS over the usual estimators in SRS.

scholarly journals Ameliorated Class of Estimators of Finite Population Variance

2020 ◽  
pp. 1-14
Author(s):  
J. O. Muili ◽  
E. N. Agwamba ◽  
A. B. Odeyale ◽  
A. Adebiyi
Keyword(s):  
Finite Population ◽  
Population Variance ◽  
First Order ◽  
Class Of Estimators ◽  
The Mean ◽  
Ratio Estimators ◽  
Mean Square Errors ◽  
Theoretical Results ◽  
Percentage Relative Efficiency ◽  
Taylor’S Series

A class of ratio estimators of finite population variance is proposed in this study. The properties of the proposed estimators have been derived using Taylor’s Series method up to first order of approximation. The efficiency conditions which are the mean square errors (MSEs) and percentage relative efficiency (PRE) of the proposed estimators over existing estimators have been established. The analytical illustration was also conducted to affirm the theoretical results. The results of the empirical study revealed that the proposed estimators are more efficient than the existing estimators considered in the study.

scholarly journals Some Improved Estimators of Population Mean using Two-Phase Sampling Scheme in the Presence of Non-Response

2021 ◽  
pp. 911-919
Author(s):  
Manoj Kumar Chaudhary ◽  
Amit Kumar ◽  
Gautam K. Vishwakarma
Keyword(s):  
Auxiliary Variable ◽  
Study Variable ◽  
Two Phase ◽  
Population Mean ◽  
Usual Estimator ◽  
Phase Sampling ◽  
Improved Estimators ◽  
Mean Square Errors ◽  
Theoretical Results ◽  
Two Phase Sampling

In the present paper, we have proposed some improved estimators of the population mean utilizing the information on two auxiliary variables adopting the idea of two-phase sampling under non-response. In order to propose the estimators, we have assumed that the study variable and first auxiliary variable suffer from non-response while the second (additional) auxiliary variable is free from non-response. We have derived the expressions for biases and mean square errors of the proposed estimators and compared them with that of usual estimator and some well known existing estimators of the population mean. The theoretical results have also been illustrated with some empirical data.

scholarly journals Flow Induced by Dual-Turbine of Different Diameters in a Gas-Liquid Agitation System: the Agitation and Turbulence Indices

2006 ◽  
Vol 4 (1) ◽  
Author(s):  
Daniel Garcia-Cortes ◽  
Catherine Xuereb ◽  
Patricia Taillandier ◽  
Ulises J Jauregui-Haza ◽  
Joel Bertrand
Keyword(s):  
Power Consumption ◽  
Turbulent Flows ◽  
Order Approximation ◽  
Velocity Fields ◽  
Two Phase ◽  
Global Parameter ◽  
First Order ◽  
Distribution Of Power ◽  
First Order Approximation ◽  
Different Diameters

Flow induced by a dual turbine stirred tank was characterized measuring local velocities with a LDV and drawing the main velocity fields and the maps of turbulence intensities. The hydrodynamic regime studied in all the experiments was the so-called merging flow regime. Two impeller configurations were studied. In the first one, two disk style turbine of the same dimensions (configuration A) were used, while in the second one, the dimensions of the upper turbine were 20 % proportionally smaller than those of the lower turbine (configuration B). The agitation and turbulence indices were used to evaluate, as a first order approximation, the power consumption distribution between convective and turbulent flows. The comparison of the two-phase agitation systems studied showed that configuration B seems to be more efficient than configuration A, since both induce a similar global convective flow, but the first one assures a significant reduction of power consumption. The distribution of power consumption between convective and turbulent flows was evaluated using the agitation index and a new global parameter: turbulence index.

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 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 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>

Estimation from an Under-Covered Sample in a Complex Survey for Auditing

2003 ◽  
Vol 54 (1-2) ◽  
pp. 115-120 ◽  
Author(s):  
Arijit Chaudhuri
Keyword(s):  
Survey Design ◽  
Simple Random Sampling ◽  
Complex Survey ◽  
Population Total ◽  
Government Documents ◽  
Complex Survey Design ◽  
The Mean ◽  
Mean Square Errors ◽  
Implementation Stage ◽  
Selection Of

We encounter a practical problem when from a finite population a complex survey design justifies selection of a sample in five stages. The design stipulates selecting a specified number of n(> 2) first stage units by Rao, Hartley and Cochran's (RHC, 1962) scheme followed by simple random sampling (SRS) without replacement (WOR) in each of the four subsequent stages. At the implementation stage a resource crunch calls for reducing the first stage sample size from n to m ( m < n). The consequential modifications in the methods of estimation of population total, mean and ratio of two totals and those of the variance of the first and the mean square errors of the latter two estimators are spelt out. The problem arises in the context of designing audits of Government documents by appropriate sampling.

RATIO ESTIMATION OF THE POPULATION MEAN USING AUXILIARY INFORMATION UNDER THE OPTIMAL SAMPLING DESIGN

2020 ◽  
pp. 1-12
Author(s):  
Chunxian Long ◽  
Wangxue Chen ◽  
Rui Yang ◽  
Dongsen Yao
Keyword(s):  
Sampling Design ◽  
Auxiliary Information ◽  
Cost Effective ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Study Variable ◽  
Population Mean ◽  
Optimal Sampling Design ◽  
Extreme Ranked Set Sampling

Cost-effective sampling design is a problem of major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time-consuming. In this article, we investigate ratio-type estimators of the population mean of the study variable, involving either the first or the third quartile of the auxiliary variable, using ranked set sampling (RSS) and extreme ranked set sampling (ERSS) schemes. The properties of the estimators are obtained. The estimators in RSS and ERSS are compared to their counterparts in simple random sampling (SRS) for normal data. The numerical results show that the estimators in RSS and ERSS are significantly more efficient than their counterparts in SRS.

scholarly journals Adapted Factor-Type Imputation Strategies

2016 ◽  
Vol 8 (3) ◽  
pp. 321-339
Author(s):  
R. Pandey ◽  
K. Yadav ◽  
N. S. Thakur
Keyword(s):  
Relative Efficiency ◽  
Mean Squared Error ◽  
Data Sets ◽  
Optimum Conditions ◽  
Population Mean ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
The Mean ◽  
Factor Type ◽  
Percentage Relative Efficiency

The present paper provides alternative improved Factor-Type (F-T) estimators of population mean in presence of item non-response for the practitioners. The proposed estimators have been shown to be more efficient than the four existing estimators which are more efficient than the usual ratio and the mean estimators. Optimum conditions for minimum mean squared error are obtained for the new estimators. Empirical comparisons based on three different data sets establish that the proposed estimators record least mean squared error and hence a substantial gain in Percentage Relative Efficiency (P.R.E.), over these five contemporary estimators.

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