Sample Survey Theory vs. General Statistical Theory: Estimation of the Population Mean

1972 ◽  
Vol 40 (1) ◽  
pp. 1 ◽  
Author(s):  
Carl-Erik Särndal ◽  
Carl-Erik Sarndal
Keyword(s):  
Statistical Theory ◽  
Sample Survey ◽  
Population Mean ◽  
General Statistical

Appendix F: General Statistical Theory

2016 ◽  
pp. 503-521
Author(s):  
Karl G. Jöreskog ◽  
Ulf H. Olsson ◽  
Fan Y. Wallentin
Keyword(s):  
Statistical Theory ◽  
General Statistical

scholarly journals Estimating a Finite Population Mean Using Transformed Data in Presence of Random Nonresponse

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nelson Kiprono Bii ◽  
Christopher Ouma Onyango ◽  
John Odhiambo
Keyword(s):  
Finite Population ◽  
Mean Squared Error ◽  
Simulated Data ◽  
Sampling Technique ◽  
Theory And Practice ◽  
Sample Survey ◽  
Population Mean ◽  
Data Weighting ◽  
Asymptotic Mean Squared Error ◽  
Mean Variance

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.

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

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.

scholarly journals Modified Regression Type Estimators in the Presence of Non-Response in Two Phase sampling

2018 ◽  
Vol 6 (07) ◽  
Author(s):  
John Kung’u Wanjiru ◽  
Grace Chumba
Keyword(s):  
Single Phase ◽  
Complete Response ◽  
Auxiliary Variable ◽  
Degree Of Approximation ◽  
Sample Survey ◽  
Sampling Scheme ◽  
Two Phase ◽  
Population Mean ◽  
Phase Sampling ◽  
Two Phase Sampling

It is a common experience in sample survey that data cannot always be collected for all units selected in the sample at the first attempt and even after some call-backs. An estimate obtained from such incomplete data may be misleading because of the non-response in the data. In addition, the population mean of the auxiliary variable from the previous census may not be available. In this paper, Modified regression type estimators proposed by Tum et al. (2014) in single phase sampling, assuming complete response, have been proposed to estimate the population mean of the study variable in the presence of non-response under two phase sampling scheme. The expression of mean squared errors (MSE) based on the proposed estimators have been derived under two phase sampling to the first degree of approximation. A comparison of the proposed estimators with the usual unbiased estimator and existing estimators under two phase sampling scheme have been carried out. The proposed Modified regression type estimators have been found to be the most efficient compared to the existing estimators and they are recommended for use in practice.

Sign in / Sign up

Export Citation Format

Share Document