Finite population sampling and inference : a prediction approach

By R. Valliant, Alan Dorfman, R. Royall. 2000

422 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4f8e107f2142de309775a2fa9b6840e9e512b2c0

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Citations in the corpus

NON-PARAMETRIC REGRESSION FOR ESTIMATING TOTALS IN FINITE POPULATIONS (2002, background): “(See Valliant, Dorfman, and Royall (2000) for a detailed account.)”
A sample-and-clean framework for fast and accurate query processing on dirty data (2014, background): “Result Estimation and Sampling: Estimating aggregate statistics of populations from samples has been well studied in the field of surveying [7,29,38,49,53,56].”
Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories (2009, methodology): “A clever use of this ancillary information for stratification, regression, or calibration can reduce the probability-based mean square error (MSE) of desired estimates (D'Orazio et al., 2006; Wu, 2003).”
Calibrated Bayes, an alternative inferential paradigm for official statistics (2012, methodology): “In superpopulation modeling (e.g. Royall 1970; Thompson 1988; Valliant, Dorfman, and Royall 2000), the population values of Y are assumed to be a random sample from a “superpopulation”, and assigned a probability distribution p Y .”
Sampling designs for accuracy assessment of land cover (2009, background): “Randomization in selecting the sample may still be justified to insure impartiality and to avoid unconscious or conscious bias in sample selection (Valliant et al. 2000, p. 20), but randomization is not needed to support model-based inference.”
To Model or Not To Model? Competing Modes of Inference for Finite Population Sampling (2004, unknown): “In superpopulation modeling (e.g., Royall 1970; Thompson 1988; Valliant, Dorfman, and Royall 2000), the populationvalues of Y are assumed to be a random sample from a “superpopulation” and are assigned a probability distribution p.Yjµ / indexed by xed parameters µ .”
Calibrated Bayes (2006, background): “Valliant, Dorfman, and Royall (2000) questioned Hansen et al.’s choice of diagnostics, but my view is that under disproportionate stratified sampling, a model like (4) that ignores stratum effects is too vulnerable to misspecification to be a sound basis for inference, unless there are convincing…”
Variance approximation under balanced sampling (2005, unknown): “Several partial solutions had been presented in Deville et al. (1988), Deville (1992), Ardilly (1991), Hedayat and Majumdar (1995), and Valliant et al. (2000).”
From start to finish: a framework for the production of small area official statistics (2018, unknown): “The approach is largely the same with model-based prediction (Valliant et al., 2000).”
Efficient balanced sampling: The cube method (2004, unknown): “Recently, Valliant et al. (2000) surveyed some existing methods.”