• Corpus ID: 220347385

A method to find an efficient and robust sampling strategy under model uncertainty

@article{Bueno2020AMT,
  title={A method to find an efficient and robust sampling strategy under model uncertainty},
  author={Edgar Bueno and Dan Hedlin},
  journal={arXiv: Methodology},
  year={2020}
}
We consider the problem of deciding on sampling strategy, in particular sampling design. We propose a risk measure, whose minimizing value guides the choice. The method makes use of a superpopulation model and takes into account uncertainty about its parameters. The method is illustrated with a real dataset, yielding satisfactory results. As a baseline, we use the strategy that couples probability proportional-to-size sampling with the difference estimator, as it is known to be optimal when the… 

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SHOWING 1-10 OF 22 REFERENCES
Optimal sampling and estimation strategies under the linear model
In some cases model-based and model-assisted inferences can lead to very different estimators. These two paradigms are not so different if we search for an optimal strategy rather than just an
A unified approach to robust estimation in finite population sampling
We argue that the conditional bias associated with a sample unit can be a useful measure of influence in finite population sampling. We use the conditional bias to derive robust estimators that are
Robust Estimation in Finite Populations I
Abstract This is an application of a least-squares prediction approach to finite population sampling theory. One way in which this approach differs from the conventional one is its focus on
Robust model‐based stratification sampling designs
We address the resistance, somewhat pervasive within the sampling community, to model‐based methods. We do this by introducing notions of “approximate models” and then deriving sampling methods which
A Generalization of Sampling Without Replacement from a Finite Universe
Abstract This paper presents a general technique for the treatment of samples drawn without replacement from finite universes when unequal selection probabilities are used. Two sampling schemes are
Robust Lavallée-Hidiroglou stratified sampling strategy
There are several reasons why robust regression techniques are useful tools in sampling design. First of all, when stratified samples are considered, one needs to deal with three main issues: the
Finite Population Sampling with Multivariate Auxiliary Information
Abstract This article examines strategies that are approximately design-unbiased and nearly optimal, assuming a large-sample survey and a regression superpopulation model. A new class of predictors
Probability Sampling Designs: Principles for Choice of Design and Balancing
TLDR
Three theoretical principles are formalized: randomization, overrepresentation and restriction; these principles are developed and used in choosing the sampling design in a systematic way and can be applied in order to improve inference.
Survey Design under the Regression Superpopulation Model
Abstract The construction of sample designs and estimators under a linear regression superpopulation model is considered. The anticipated variance, the variance of the predictor computed with respect
Some results on generalized difference estimation and generalized regression estimation for finite populations
Let S = {s} be the set of subsets of {1, ..., N} such that each s eS contains n elements. We consider in this paper only designs p(s) with fixed sample size, that is P(S) > 0 only if s eS and ESp(s)
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