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Abstract:
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Informative sampling designs are broadly used across many application areas of statistical modeling and can have a large impact on model inference and prediction. In spatial modeling, informative sampling can result in biased spatial covariance parameter estimation, which in turn can bias spatial prediction. To mitigate these biases, we develop a weighted composite likelihood approach to improve spatial covariance parameter estimation under informative sampling designs. Then, given these parameter estimates, we propose two approaches to quantify the effects of the sampling design on the variance estimates in spatial prediction in order to make informed decisions for population-based inference.
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