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Technometrics Volume 57, 2015 - Issue 1
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Original Articles

Model-Based Sampling Design for Multivariate Geostatistics

Jie LiDepartment of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA24601 ([email protected])
&
Dale L. ZimmermanDepartment of Statistics and Actuarial Science, 241 Schaeffer Hall, University of Iowa, Iowa City, IA52242 ([email protected])
Pages 75-86 | Received 01 Mar 2013, Published online: 04 Mar 2015

Citations (10)

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Read on this site (3)

Seokhyun Chung & Raed Al Kontar. (2024) Federated Multi-Output Gaussian Processes. Technometrics 66:1, pages 90-103.
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Yibo Sun, Xiuyun Meng, Teng Long & Yufei Wu. (2020) A fast optimal Latin hypercube design method using an improved translational propagation algorithm. Engineering Optimization 52:7, pages 1244-1260.
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Firoozeh Rivaz. (2016) Optimal network design for Bayesian spatial prediction of multivariate non-Gaussian environmental data. Journal of Applied Statistics 43:7, pages 1335-1348.
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Articles from other publishers (7)

Subhadra Dasgupta, Siuli Mukhopadhyay & Jonathan Keith. (2024) G‐optimal grid designs for kriging models. Scandinavian Journal of Statistics.
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Zahra Samadi, Hooshang Talebi & Firoozeh Rivaz. (2023) Bayesian spatial optimal network design for skew Gaussian environmental processes. Stochastic Environmental Research and Risk Assessment 37:8, pages 2993-3007.
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Subhadra Dasgupta, Siuli Mukhopadhyay & Jonathan Keith. (2022) Optimal designs for some bivariate cokriging models. Journal of Statistical Planning and Inference 221, pages 9-28.
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Dale L. Zimmerman. 2014. Wiley StatsRef: Statistics Reference Online. Wiley StatsRef: Statistics Reference Online 1 10 .
G. Nishani Musafer & M. Helen Thompson. (2016) Non-linear optimal multivariate spatial design using spatial vine copulas. Stochastic Environmental Research and Risk Assessment 31:2, pages 551-570.
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Martha Bohorquez, Ramon Giraldo & Jorge Mateu. (2016) Optimal dynamic spatial sampling. Environmetrics 27:5, pages 293-305.
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Dale L. Zimmerman. (2015) Discussion of ‘Efficient prediction designs for random fields’. Applied Stochastic Models in Business and Industry 31:2, pages 195-197.
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