IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v66y2017i5p1031-1047.html
   My bibliography  Save this article

Ecological grouping of survey sites when sampling artefacts are present

Author

Listed:
  • Scott D. Foster
  • Nicole A. Hill
  • Mitchell Lyons

Abstract

No abstract is available for this item.

Suggested Citation

  • Scott D. Foster & Nicole A. Hill & Mitchell Lyons, 2017. "Ecological grouping of survey sites when sampling artefacts are present," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 1031-1047, November.
    • Handle: RePEc:bla:jorssc:v:66:y:2017:i:5:p:1031-1047
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssc.12211
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Jukka Corander & Jukka Sirén & Elja Arjas, 2008. "Bayesian spatial modeling of genetic population structure," Computational Statistics, Springer, vol. 23(1), pages 111-129, January.
    2. Dunstan, Piers K. & Foster, Scott D. & Darnell, Ross, 2011. "Model based grouping of species across environmental gradients," Ecological Modelling, Elsevier, vol. 222(4), pages 955-963.
    3. S.D. Foster & G.H. Givens & G.J. Dornan & P.K. Dunstan & R. Darnell, 2013. "Modelling biological regions from multi‐species and environmental data," Environmetrics, John Wiley & Sons, Ltd., vol. 24(7), pages 489-499, November.
    4. Pledger, Shirley & Arnold, Richard, 2014. "Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 241-261.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jarno Vanhatalo & Scott D. Foster & Geoffrey R. Hosack, 2021. "Spatiotemporal clustering using Gaussian processes embedded in a mixture model," Environmetrics, John Wiley & Sons, Ltd., vol. 32(7), November.

    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. Jarno Vanhatalo & Scott D. Foster & Geoffrey R. Hosack, 2021. "Spatiotemporal clustering using Gaussian processes embedded in a mixture model," Environmetrics, John Wiley & Sons, Ltd., vol. 32(7), November.
    2. Brian J. Reich & Howard D. Bondell, 2011. "A Spatial Dirichlet Process Mixture Model for Clustering Population Genetics Data," Biometrics, The International Biometric Society, vol. 67(2), pages 381-390, June.
    3. Fernández, D. & Arnold, R. & Pledger, S., 2016. "Mixture-based clustering for the ordered stereotype model," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 46-75.
    4. Eleni Matechou & Ivy Liu & Daniel Fernández & Miguel Farias & Bergljot Gjelsvik, 2016. "Biclustering Models for Two-Mode Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 81(3), pages 611-624, September.
    5. D. Fernández & S. Pledger, 2016. "Categorising Count Data into Ordinal Responses with Application to Ecological Communities," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 348-362, June.
    6. Wen‐Han Hwang & Richard Huggins & Jakub Stoklosa, 2022. "A model for analyzing clustered occurrence data," Biometrics, The International Biometric Society, vol. 78(2), pages 598-611, June.
    7. Hui, Francis K.C., 2017. "Model-based simultaneous clustering and ordination of multivariate abundance data in ecology," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 1-10.
    8. Christian Carmona & Luis Nieto-Barajas & Antonio Canale, 2019. "Model-based approach for household clustering with mixed scale variables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 559-583, June.
    9. Jacques, Julien & Biernacki, Christophe, 2018. "Model-based co-clustering for ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 101-115.
    10. M. P. B. Gallaugher & C. Biernacki & P. D. McNicholas, 2023. "Parameter-wise co-clustering for high-dimensional data," Computational Statistics, Springer, vol. 38(3), pages 1597-1619, September.
    11. Emilio Carrizosa & Vanesa Guerrero & Dolores Romero Morales, 2023. "On mathematical optimization for clustering categories in contingency tables," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 407-429, June.
    12. Álvarez de Toledo, Pablo & Núñez, Fernando & Usabiaga, Carlos, 2018. "Matching and clustering in square contingency tables. Who matches with whom in the Spanish labour market," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 135-159.
    13. Tatjana Miljkovic & Daniel Fernández, 2018. "On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio," Risks, MDPI, vol. 6(2), pages 1-18, May.
    14. Daniel Fernández & Radim J. Sram & Miroslav Dostal & Anna Pastorkova & Hans Gmuender & Hyunok Choi, 2018. "Modeling Unobserved Heterogeneity in Susceptibility to Ambient Benzo[ a ]pyrene Concentration among Children with Allergic Asthma Using an Unsupervised Learning Algorithm," IJERPH, MDPI, vol. 15(1), pages 1-18, January.
    15. Daniel Fernández & Richard Arnold & Shirley Pledger & Ivy Liu & Roy Costilla, 2019. "Finite mixture biclustering of discrete type multivariate data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 117-143, March.
    16. Pledger, Shirley & Arnold, Richard, 2014. "Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 241-261.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jorssc:v:66:y:2017:i:5:p:1031-1047. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.