IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v1y2020i3d10.1007_s43069-020-0011-z.html
   My bibliography  Save this article

An R Package for Generating Covariance Matrices for Maximum-Entropy Sampling from Precipitation Chemistry Data

Author

Listed:
  • Hessa Al-Thani

    (University of Michigan)

  • Jon Lee

    (University of Michigan)

Abstract

We present an open-source R package (MESgenCov v 0.1.0) for temporally fitting multivariate precipitation chemistry data and extracting a covariance matrix for use in the MESP (maximum-entropy sampling problem). We provide multiple functionalities for modeling and model assessment. The package is tightly coupled with NADP/NTN (National Atmospheric Deposition Program/National Trends Network) data from their set of 379 monitoring sites, 1978–present. The user specifies the sites, chemicals, and time period desired, fits an appropriate user-specified univariate model for each site and chemical selected, and the package produces a covariance matrix for use by MESP algorithms.

Suggested Citation

  • Hessa Al-Thani & Jon Lee, 2020. "An R Package for Generating Covariance Matrices for Maximum-Entropy Sampling from Precipitation Chemistry Data," SN Operations Research Forum, Springer, vol. 1(3), pages 1-21, September.
    • Handle: RePEc:spr:snopef:v:1:y:2020:i:3:d:10.1007_s43069-020-0011-z
    DOI: 10.1007/s43069-020-0011-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-020-0011-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s43069-020-0011-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. J. V. Zidek & W. Sun & N. D. Le, 2000. "Designing and integrating composite networks for monitoring multivariate gaussian pollution fields," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(1), pages 63-79.
    2. P. Sebastiani & H. P. Wynn, 2000. "Maximum entropy sampling and optimal Bayesian experimental design," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 145-157.
    3. Kurt M. Anstreicher, 2018. "Maximum-entropy sampling and the Boolean quadric polytope," Journal of Global Optimization, Springer, vol. 72(4), pages 603-618, December.
    4. Chun-Wa Ko & Jon Lee & Maurice Queyranne, 1995. "An Exact Algorithm for Maximum Entropy Sampling," Operations Research, INFORMS, vol. 43(4), pages 684-691, August.
    5. ANSTREICHER, Kurt M. & FAMPA, Marcia & LEE, Jon & WILLIAMS, Joy, 1999. "Using continuous nonlinear relaxations to solve constrained maximum-entropy sampling problems," LIDAM Reprints CORE 1412, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Jon Lee, 1998. "Constrained Maximum-Entropy Sampling," Operations Research, INFORMS, vol. 46(5), pages 655-664, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhongzhu Chen & Marcia Fampa & Jon Lee, 2023. "On Computing with Some Convex Relaxations for the Maximum-Entropy Sampling Problem," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 368-385, March.
    2. Kurt M. Anstreicher, 2020. "Efficient Solution of Maximum-Entropy Sampling Problems," Operations Research, INFORMS, vol. 68(6), pages 1826-1835, November.
    3. Kurt M. Anstreicher, 2018. "Maximum-entropy sampling and the Boolean quadric polytope," Journal of Global Optimization, Springer, vol. 72(4), pages 603-618, December.
    4. HOFFMAN, Alan & LEE, Jon & WILLIAMS, Joy, 2000. "New upper bounds for maximum-entropy sampling," LIDAM Discussion Papers CORE 2000012, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. repec:dgr:rugsom:99a17 is not listed on IDEAS
    6. Goldengorin, Boris & Tijssen, Gert A. & Tso, Michael, 1999. "The maximization of submodular functions : old and new proofs for the correctness of the dichotomy algorithm," Research Report 99A17, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    7. Goldengorin, Boris, 2009. "Maximization of submodular functions: Theory and enumeration algorithms," European Journal of Operational Research, Elsevier, vol. 198(1), pages 102-112, October.
    8. Boris Goldengorin & Gerard Sierksma & Gert A. Tijssen & Michael Tso, 1999. "The Data-Correcting Algorithm for the Minimization of Supermodular Functions," Management Science, INFORMS, vol. 45(11), pages 1539-1551, November.
    9. Yildiz, Anil & Mern, John & Kochenderfer, Mykel J. & Howland, Michael F., 2023. "Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit," Renewable Energy, Elsevier, vol. 216(C).
    10. Linda Altieri & Daniela Cocchi, 2021. "Spatial Sampling for Non‐compact Patterns," International Statistical Review, International Statistical Institute, vol. 89(3), pages 532-549, December.
    11. Jun Yu & HaiYing Wang, 2022. "Subdata selection algorithm for linear model discrimination," Statistical Papers, Springer, vol. 63(6), pages 1883-1906, December.
    12. Kim, Taejin & Lee, Gueseok & Youn, Byeng D., 2019. "PHM experimental design for effective state separation using Jensen–Shannon divergence," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    13. Han, Cong & Chaloner, Kathryn, 2004. "A note on optimal designs for two or more treatment groups," Statistics & Probability Letters, Elsevier, vol. 69(1), pages 81-89, August.
    14. Kasianova, Ksenia & Kelbert, Mark & Mozgunov, Pavel, 2021. "Response adaptive designs for Phase II trials with binary endpoint based on context-dependent information measures," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    15. Rusin, K. & Wróblewski, W. & Rulik, S., 2021. "Efficiency based optimization of a Tesla turbine," Energy, Elsevier, vol. 236(C).
    16. Rodriguez, Sergio & Ludkovski, Michael, 2020. "Probabilistic bisection with spatial metamodels," European Journal of Operational Research, Elsevier, vol. 286(2), pages 588-603.
    17. Xuesong Zhou & George F. List, 2010. "An Information-Theoretic Sensor Location Model for Traffic Origin-Destination Demand Estimation Applications," Transportation Science, INFORMS, vol. 44(2), pages 254-273, May.
    18. Yong Tan & Amir Karbassi Yazdi & Jorge Antunes & Peter Wanke & Angappa Gunasekaran & Henrique Luiz Corrêa & Giuliani Coluccio, 2024. "The Quest for an ESG Country Rank: A Performance Contribution Analysis/MCDM Approach," Mathematics, MDPI, vol. 12(12), pages 1-24, June.
    19. Belmiro P. M. Duarte & Anthony C. Atkinson & Satya P. Singh & Marco S. Reis, 2023. "Optimal design of experiments for hypothesis testing on ordered treatments via intersection-union tests," Statistical Papers, Springer, vol. 64(2), pages 587-615, April.
    20. Theodore T. Allen & Olivia K. Hernand & Abdullah Alomair, 2020. "Optimal Off-line Experimentation for Games," Decision Analysis, INFORMS, vol. 17(4), pages 277-298, December.
    21. Yi Liu & Gavin Shaddick & James V. Zidek, 2017. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 559-581, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snopef:v:1:y:2020:i:3:d:10.1007_s43069-020-0011-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.