Robustness in Survey Sampling Using the Conditional Bias Approach with R Implementation

2018 ◽  
pp. 3-13
Author(s):  
Cyril Favre-Martinoz ◽  
Anne Ruiz-Gazen ◽  
Jean Francois Beaumont ◽  
David Haziza
Keyword(s):  
Survey Sampling

An Efficient Class of Calibration Ratio Estimators of Domain Mean in Survey Sampling

2019 ◽  
Vol 8 (3) ◽  
pp. 279-293
Author(s):  
Ekaette I. Enang ◽  
Etebong P. Clement
Keyword(s):  
Survey Sampling ◽  
Ratio Estimators

Design-based and model-based inference in survey sampling: appreciating the difference

1998 ◽  
Vol 28 (10) ◽  
pp. 1429-1447 ◽  
Author(s):  
T G Gregoire
Keyword(s):  
Finite Population ◽  
Survey Sampling ◽  
Finite Population Sampling ◽  
Population Sampling ◽  
Model Based ◽  
The Difference

Model-based ideas in finite-population sampling have received renewed discussion in recent years.Their relationship to the classical ideas in sampling theorydo not appear to be universally well understood by samplers in applied disciplines such as forestry, and ecology more broadly.The two inferential paradigms are constrasted, andexplanations are supplemented with examples of discrete aswell as continuously distributed populations. The treatment of spatial structureis examined, also.

Bayesian sufficiency in survey-sampling

1968 ◽  
Vol 20 (1) ◽  
pp. 363-373 ◽  
Author(s):  
V. P. Godambe
Keyword(s):  
Survey Sampling

A Review of the Cluster Survey Sampling Method in Humanitarian Emergencies

2008 ◽  
Vol 25 (4) ◽  
pp. 370-374 ◽  
Author(s):  
Shaun K. Morris ◽  
Claire K. Nguyen
Keyword(s):  
Sampling Method ◽  
Survey Sampling ◽  
Humanitarian Emergencies ◽  
Cluster Survey

scholarly journals Bayesian Analysis of Finite Populations under Simple Random Sampling

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 318
Author(s):  
Manuel Mendoza ◽  
Alberto Contreras-Cristán ◽  
Eduardo Gutiérrez-Peña
Keyword(s):  
Bayesian Analysis ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Survey Sampling ◽  
Finite Populations ◽  
Population Means ◽  
Sampling Schemes ◽  
Nonparametric Approach ◽  
Conventional Procedure ◽  
Continuous And Discrete Variables

Statistical methods to produce inferences based on samples from finite populations have been available for at least 70 years. Topics such as Survey Sampling and Sampling Theory have become part of the mainstream of the statistical methodology. A wide variety of sampling schemes as well as estimators are now part of the statistical folklore. On the other hand, while the Bayesian approach is now a well-established paradigm with implications in almost every field of the statistical arena, there does not seem to exist a conventional procedure—able to deal with both continuous and discrete variables—that can be used as a kind of default for Bayesian survey sampling, even in the simple random sampling case. In this paper, the Bayesian analysis of samples from finite populations is discussed, its relationship with the notion of superpopulation is reviewed, and a nonparametric approach is proposed. Our proposal can produce inferences for population quantiles and similar quantities of interest in the same way as for population means and totals. Moreover, it can provide results relatively quickly, which may prove crucial in certain contexts such as the analysis of quick counts in electoral settings.

Corrigenda: Survey Sampling.

1969 ◽  
Vol 64 (328) ◽  
pp. 1699 ◽  
Author(s):  
Leslie Kish
Keyword(s):  
Survey Sampling

Targeted Learning Using Adaptive Survey Sampling

2018 ◽  
pp. 541-559
Author(s):  
Antoine Chambaz ◽  
Emilien Joly ◽  
Xavier Mary
Keyword(s):  
Survey Sampling

scholarly journals Survey sampling and small-area estimation

2018 ◽  
Vol 25 (4) ◽  
pp. 181-183
Author(s):  
Enrico Fabrizi ◽  
Tomasz Ża̧dło
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Survey Sampling ◽  
Area Estimation
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