Spatial sampling design for prediction with estimated parameters

By Michael L. Stein. 2006

202 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/477ddb7be7a1f505bc1e146b7de07d28ba1e218b

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Citations in the corpus

Bayesian optimization for sensor set selection (2010, background): “Here the amount of work for moving a pointisproportional toitsweight and thedistancetraveled.”
Near-optimal sensor placements in Gaussian processes (2005, unknown): “Alternatively, if we can characterize the uncertainty about the parameters of the model, we can explicitly optimize the placements over possible models (Zidek et al., 2000; Zimmerman, 2006; Zhu and Stein, 2006).”
Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests (2008, background): “…by Müller and Zimmerman (1999), Warrick and Myers (1987), and Russo (1984); the estimation of spatially referenced LR models by Lesch (2005) and Lesch et al. (1995), and the estimation of geostatistical linear models by Brus and Heuvelink (2007), Minasny et al. (2007), and Zhu and Stein (2006).”
Optimization of sample patterns for universal kriging of environmental variables (2007, methodology): “Also, one may incorporate the uncertainty about the UK-model in the sampling method as described by Zhu and Stein (2006).”
A Resampling-Based Stochastic Approximation Method for Analysis of Large Geostatistical Data (2013, result): “It is interesting to point out that Zhu and Stein (2005) conjectured that MLE has the same result as given in (19).”
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach (2007, background): “Zhu and Stein (2006) present an approach for a priori design for spatial prediction in the case of unknown parameters.”
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies (2008, unknown): “Alternatively, if we can characterize the uncertainty about the parameters of the model, we can explicitly optimize the placements over possible models (Zidek et al., 2000; Zimmerman, 2006; Zhu and Stein, 2006).”
Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure (2016, methodology): “The estimation-adjusted (EA) criterion implemented by Zhu and Stein (2006) is similar in spirit to the EK criterion.”
Spatial sampling design and covariance-robust minimax prediction based on convex design ideas (2010, background): “But up to now in the literature either criteria for estimation of the covariance function or for prediction have been proposed, with only few exceptions: Zhu and Stein (2006) consider the minimization of the length of prediction intervals as design criterion and thereby combine both prediction and”