IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v58y2009i2p267-284.html
   My bibliography  Save this article

A functional approach to diversity profiles

Author

Listed:
  • Stefano A. Gattone
  • Tonio Di Battista

Abstract

Summary. Diversity plays a central role in ecological theory and its conservation and management are important issues for the wellbeing and stability of ecosystems. The aim of this work is to provide a reliable theoretical framework for performing statistical analysis on ecological diversity by means of the joint use of diversity profiles and functional data analysis. We point out that ecological diversity is a multivariate concept as it is a function of the relative abundances of species in a biological community. For this, several researchers have suggested using parametric families of indices of diversity for obtaining more information from the data. Patil and Taillie introduced the concept of intrinsic diversity ordering which can be determined by using the diversity profile. It may be noted that the diversity profile is a non‐negative and convex curve which consists of a sequence of measurements as a function of a given parameter. Thus, diversity profiles can be explained through a process that is described in a functional setting. Recent developments in environmental studies have focused on the opportunity to evaluate community diversity changes over space and/or correlation of diversity with environmental characteristics. For this, we develop an innovative analysis of diversity based on a functional data approach. Whereas conventional statistical methods process data as a sequence of individual observations, functional data analysis is designed to process a collection of functions or curves. Moreover, unconstrained models may lead to negative and/or non‐convex estimates for the diversity profiles. To overcome this problem, a transformation is proposed which can be constrained to be non‐negative and convex. We focus on some applications showing how functional data analysis provides an alternative way of understanding biological diversity and its interaction with natural and/or human factors.

Suggested Citation

  • Stefano A. Gattone & Tonio Di Battista, 2009. "A functional approach to diversity profiles," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(2), pages 267-284, May.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:2:p:267-284
    DOI: 10.1111/j.1467-9876.2009.00646.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9876.2009.00646.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1467-9876.2009.00646.x?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
    ---><---

    References listed on IDEAS

    as
    1. Dole, David, 1999. "CoSmo: A Constrained Scatterplot Smoother for Estimating Convex, Monotonic Transformations," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(4), pages 444-455, October.
    2. J. O. Ramsay, 1998. "Estimating smooth monotone functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 365-375.
    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. Fabrizio Maturo & Stefania Migliori & Francesco Paolone, 2019. "Measuring and monitoring diversity in organizations through functional instruments with an application to ethnic workforce diversity of the U.S. Federal Agencies," Computational and Mathematical Organization Theory, Springer, vol. 25(4), pages 357-388, December.
    2. Francesca Fortuna & Stefano Antonio Gattone & Tonio Di Battista, 2020. "Functional estimation of diversity profiles," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.
    3. Fabrizio Maturo & Antonio Balzanella & Tonio Di Battista, 2019. "Building Statistical Indicators of Equitable and Sustainable Well-Being in a Functional Framework," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(3), pages 449-471, December.
    4. Gattone, Stefano Antonio & Fortuna, Francesca & Evangelista, Adelia & Di Battista, Tonio, 2022. "Simultaneous confidence bands for the functional mean of convex curves," Econometrics and Statistics, Elsevier, vol. 24(C), pages 183-193.
    5. Natalia Golini & Rosaria Ignaccolo & Luigi Ippoliti & Nicola Pronello, 2025. "Functional zoning of biodiversity profiles," Environmetrics, John Wiley & Sons, Ltd., vol. 36(1), January.

    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. Charu Sharma & Amber Habib & Sunil Bowry, 2018. "Cluster analysis of stocks using price movements of high frequency data from National Stock Exchange," Papers 1803.09514, arXiv.org.
    2. Shively, Thomas S. & Kockelman, Kara & Damien, Paul, 2010. "A Bayesian semi-parametric model to estimate relationships between crash counts and roadway characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 699-715, June.
    3. Gattone, Stefano Antonio & Fortuna, Francesca & Evangelista, Adelia & Di Battista, Tonio, 2022. "Simultaneous confidence bands for the functional mean of convex curves," Econometrics and Statistics, Elsevier, vol. 24(C), pages 183-193.
    4. repec:jss:jstsof:18:i04 is not listed on IDEAS
    5. Boudaoud, S. & Rix, H. & Meste, O., 2010. "Core Shape modelling of a set of curves," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 308-325, February.
    6. C Rohrbeck & D A Costain & A Frigessi, 2018. "Bayesian spatial monotonic multiple regression," Biometrika, Biometrika Trust, vol. 105(3), pages 691-707.
    7. J. O. Ramsay & G. Hooker & D. Campbell & J. Cao, 2007. "Parameter estimation for differential equations: a generalized smoothing approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 741-796, November.
    8. David BENATIA & Etienne BILLETTE de VILLEMEUR, 2019. "Strategic Reneging in Sequential Imperfect Markets," Working Papers 2019-19, Center for Research in Economics and Statistics.
    9. Cai, Bo & Dunson, David B., 2007. "Bayesian Multivariate Isotonic Regression Splines: Applications to Carcinogenicity Studies," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1158-1171, December.
    10. Mauricio Lopez-Mendez & Rowan Iskandar & Eric Jutkowitz, 2023. "Individual and Dyadic Health-Related Quality of Life of People Living with Dementia and their Caregivers," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 18(4), pages 1673-1692, August.
    11. Gabriel Riutort-Mayol & Virgilio Gómez-Rubio & José Luis Lerma & Julio M. del Hoyo-Meléndez, 2020. "Correlated Functional Models with Derivative Information for Modeling Microfading Spectrometry Data on Rock Art Paintings," Mathematics, MDPI, vol. 8(12), pages 1-25, December.
    12. Härdle, Wolfgang & Yatchew, Adonis, 2001. "Dynamic nonparametric state price density estimation using constrained least squares and the bootstrap," SFB 373 Discussion Papers 2002,16, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    13. Ian Fillmore, 2021. "Price Discrimination and Public Policy in the U.S. College Market," Working Papers 2021-028, Human Capital and Economic Opportunity Working Group.
    14. Wu, Ximing & Sickles, Robin, 2018. "Semiparametric estimation under shape constraints," Econometrics and Statistics, Elsevier, vol. 6(C), pages 74-89.
    15. Björn Bornkamp & Katja Ickstadt, 2009. "Bayesian Nonparametric Estimation of Continuous Monotone Functions with Applications to Dose–Response Analysis," Biometrics, The International Biometric Society, vol. 65(1), pages 198-205, March.
    16. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    17. John Haslett & Andrew Parnell, 2008. "A simple monotone process with application to radiocarbon‐dated depth chronologies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(4), pages 399-418, September.
    18. Ng, Kenyon & Turlach, Berwin A. & Murray, Kevin, 2019. "A flexible sequential Monte Carlo algorithm for parametric constrained regression," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 13-26.
    19. Zhang, Ruizhi & Wang, Jian & Mei, Yajun, 2017. "Search for evergreens in science: A functional data analysis," Journal of Informetrics, Elsevier, vol. 11(3), pages 629-644.
    20. Zhou, He & Zou, Hui, 2024. "The nonparametric Box–Cox model for high-dimensional regression analysis," Journal of Econometrics, Elsevier, vol. 239(2).
    21. Kim, Yuwon & Koo, Ja-Yong, 2005. "Inverse boosting for monotone regression functions," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 757-770, June.

    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:58:y:2009:i:2:p:267-284. 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.