Erratum to some improved ratio type estimators of population mean and ratio in finite population sample survey

Developing finite population estimators of parameters such as mean, variance, and asymptotic mean squared error has been one of the core objectives of sample survey theory and practice. Sample survey practitioners need to assess the properties of these estimators so that better ones can be adopted. In survey sampling, the occurrence of nonresponse affects inference and optimality of the estimators of finite population parameters. It introduces bias and may cause samples to deviate from the distributions obtained by the original sampling technique. To compensate for random nonresponse, imputation methods have been proposed by various researchers. However, the asymptotic bias and variance of the finite population mean estimators are still high under this technique. In this paper, transformation of data weighting technique is suggested. The proposed estimator is observed to be asymptotically consistent under mild assumptions. Simulated data show that the estimator proposed is much better than its rival estimators for all the different mean functions simulated.

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Some improved ratio type estimators of population mean and ratio in finite population sample surveys

Communication in Statistics- Theory and Methods ◽

10.1080/03610928908829905 ◽

1989 ◽

Vol 18 (1) ◽

pp. 379-392 ◽

Cited By ~ 24

Author(s):

B. Prasad

Keyword(s):

Finite Population ◽

Population Sample ◽

Sample Surveys ◽

Population Mean

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Estimation of Finite Population Mean in Multivariate Stratified Sampling under Cost Function Using Goal Programming

Journal of Applied Mathematics ◽

10.1155/2014/686579 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7

Author(s):

Atta Ullah ◽

Javid Shabbir ◽

Zawar Hussain ◽

Bander Al-Zahrani

Keyword(s):

Cost Function ◽

Sample Size ◽

Goal Programming ◽

Random Sampling ◽

Finite Population ◽

Sample Survey ◽

Programming Approach ◽

Stratified Random Sampling ◽

Practical Utilization ◽

Population Mean

In practical utilization of stratified random sampling scheme, the investigator meets a problem to select a sample that maximizes the precision of a finite population mean under cost constraint. An allocation of sample size becomes complicated when more than one characteristic is observed from each selected unit in a sample. In many real life situations, a linear cost function of a sample sizenhis not a good approximation to actual cost of sample survey when traveling cost between selected units in a stratum is significant. In this paper, sample allocation problem in multivariate stratified random sampling with proposed cost function is formulated in integer nonlinear multiobjective mathematical programming. A solution procedure is proposed using extended lexicographic goal programming approach. A numerical example is presented to illustrate the computational details and to compare the efficiency of proposed compromise allocation.

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