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"Sample design for analysis using high-influence probability sampling" by Robert G. Clark and David G. Steel

Sample designs are typically developed to estimate summary statistics such as means, proportions and prevalences. Analytical outputs may also be a priority but there are fewer methods and results on how to efficiently design samples for the fitting and estimation of statistical models. This paper develops a general approach for determining efficient sampling designs for probability-weighted maximum likelihood estimators and considers application to generalized linear models. We allow for non-ignorable sampling, including outcome-dependent sampling. The new designs have probabilities of selection closely related to influence statistics such as dfbeta and Cook's distance. The new approach is shown to perform well in a simulation based on data from the New Zealand Health Survey. ....

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Researchers at Georgia Tech Propose 'LABOR' (LAyer-neighBOR sampling), A New Sampling Algorithm-Based on Machine Learning

Researchers at Georgia Tech Propose 'LABOR' (LAyer-neighBOR sampling), A New Sampling Algorithm-Based on Machine Learning ....

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Color Stippling – Jose's Sketchbook

Color Stippling – Jose's Sketchbook
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