Tuning design weights to deal with non-response in successive sampling

2021 ◽  
Vol 16 (2) ◽  
pp. 97-108
Author(s):  
Kumari Priyanka
Keyword(s):  
Natural Population ◽  
Simulation Study ◽  
Finite Population ◽  
Auxiliary Information ◽  
Population Level ◽  
Successive Sampling ◽  
Population Mean ◽  
Design Weights

The estimation of finite population mean at current occasion in two occasion successive sampling in presence of non-response is investigated using tuned jackknife estimators. Based on the availability of auxiliary information at population level (Info U) and sample level (Info s) and using tuned jackknife technique, estimators have been proposed. Estimator of variance of proposed estimators have also been discussed. Different cases of occurance of non-response have been explored. The estimators are mutually compared. The properties of these estimators are studied via simulation study using natural population.

scholarly journals Efficient Class of Estimators for Finite Population Mean using Auxiliary Information in Two-Occasion Successive Sampling

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
G. N. Singh ◽  
Mohd Khalid
Keyword(s):  
Linear Combination ◽  
Finite Population ◽  
Auxiliary Information ◽  
Successive Sampling ◽  
Sample Mean ◽  
Population Means ◽  
Population Mean ◽  
Practical Applications ◽  
Optimum Estimator ◽  
Class Of Estimators

In the case of sampling on two occasions, a class of estimators is considered which uses information on the first occasion as well as the second occasion in order to estimate the population means on the current (second) occasion. The usefulness of auxiliary information in enhancing the efficiency of this estimation is examined through the class of proposed estimators. Some properties of the class of estimators and a strategy of optimum replacement are discussed. The proposed class of estimators were empirically compared with the sample mean estimator in the case of no matching. The established optimum estimator, which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion, was empirically compared with the proposed class of estimators. Mutual comparisons of the proposed estimator were carried out. Suitable recommendations are made to the survey statistician for practical applications.

scholarly journals Modified Exponential Ratio and Product Estimators for Finite Population Mean in Double Sampling

2016 ◽  
Vol 36 (3) ◽  
Author(s):  
Housila P. Singh ◽  
Gajendra K. Vishwakarma
Keyword(s):  
Empirical Study ◽  
Finite Population ◽  
Auxiliary Information ◽  
Double Sampling ◽  
Population Mean ◽  
Sampling Ratio

This paper presents exponential ratio and product estimators for estimating finite population mean using auxiliary information in double sampling and analyzes their properties. These estimators are compared for their precision with simple mean per unit, usual double sampling ratio and product estimators. An empirical study is also carried out to judge the merits of the suggested estimators.

scholarly journals Some Exponential Type Predictive Estimators of Finite Population Mean in Two-Phase Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 51-57
Author(s):  
Asifa Kamal ◽  
Nimra Amir ◽  
Huma Dastagir
Keyword(s):  
Simulation Study ◽  
Finite Population ◽  
Numerical Study ◽  
Real Life ◽  
Data Sets ◽  
Two Phase ◽  
Population Mean ◽  
Real Life Data ◽  
Phase Sampling ◽  
Two Phase Sampling

This study is designed for predictive estimation of finite population mean in two phase sampling using two auxiliary variables. Two phase exponential ratio type estimator and exponential chain ratio type estimator are proposed under predictive approach suggested by Bahl & Tuteja (1991). The expressions of bias and mean square error of both suggested estimators have been carried out for theoretical comparison. The numerical study on real life data sets as well as simulation study has been conducted to examine the performance of suggested estimators. Finally, it is demonstrated that the suggested exponential ratio estimators are more efficient than competitive estimators in support of numerical and simulation study as well.

Sign in / Sign up

Export Citation Format

Share Document