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

1989 ◽  
Vol 18 (1) ◽  
pp. 379-392 ◽  
Author(s):  
B. Prasad
Keyword(s):  
Finite Population ◽  
Population Sample ◽  
Sample Surveys ◽  
Population Mean

An Improved Estimators for Finite Population Mean in Sample Surveys

2018 ◽  
Vol 5 (2) ◽  
pp. 73-85
Author(s):  
Mir Subzar ◽  
S. Maqbool ◽  
T. A. Raja ◽  
Muhammad Abid ◽  
Faizan Danish
Keyword(s):  
Finite Population ◽  
Sample Surveys ◽  
Population Mean ◽  
Improved Estimators

A new class of estimators of finite population mean in sample surveys

2016 ◽  
Vol 46 (6) ◽  
pp. 2630-2637
Author(s):  
Housila P. Singh ◽  
Surya Kant Pal ◽  
Ramkrishna S. Solanki
Keyword(s):  
Finite Population ◽  
Sample Surveys ◽  
Population Mean ◽  
New Class ◽  
Class Of Estimators

scholarly journals Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Nelson Kiprono Bii ◽  
Christopher Ouma Onyango ◽  
John Odhiambo
Keyword(s):  
Finite Population ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Cluster Sampling ◽  
Sample Surveys ◽  
Population Mean ◽  
Second Stage ◽  
Kernel Weights ◽  
Squared Error ◽  
Interval Lengths

Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95% coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys.

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