IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v170y2007i3p691-711.html
   My bibliography  Save this article

Model comparison for a complex ecological system

Author

Listed:
  • C. A. Ferguson
  • A. W. Bowman
  • E. M. Scott
  • L. Carvalho

Abstract

Summary. The ecological consequences of climate change and its interaction with other environmental pressures, such as nutrient pollution, are little understood. For freshwater ecosystems, knowledge of these combined effects is required for water resource management and in particular for successful implementation of the European Community Water Framework Directive 2000, which requires that all surface‐waters should be at, or above, ‘good status’ by 2016. Statistical analysis of detailed long‐term environmental data sets can be used to explore these combined effects. Loch Leven (Scotland) has been routinely monitored since 1967, providing one of the largest and most extensive of such data sets. Over this period there has been evidence of climate change and a period of eutrophication and recovery at the loch. Transfer functions, additive models and varying‐coefficient models are used to explore the complex ecological system at Loch Leven with the aim of obtaining insight into the combined effects of climate change and eutrophication on water quality.

Suggested Citation

  • C. A. Ferguson & A. W. Bowman & E. M. Scott & L. Carvalho, 2007. "Model comparison for a complex ecological system," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 691-711, July.
    • Handle: RePEc:bla:jorssa:v:170:y:2007:i:3:p:691-711
    DOI: 10.1111/j.1467-985X.2006.00462.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-985X.2006.00462.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-985X.2006.00462.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. Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
    2. Jianqing Fan & Wenyang Zhang, 2000. "Simultaneous Confidence Bands and Hypothesis Testing in Varying‐coefficient Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 715-731, December.
    3. Yingcun Xia, 2004. "Efficient estimation for semivarying-coefficient models," Biometrika, Biometrika Trust, vol. 91(3), pages 661-681, September.
    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. Djeundje, Viani Biatat & Crook, Jonathan, 2019. "Dynamic survival models with varying coefficients for credit risks," European Journal of Operational Research, Elsevier, vol. 275(1), pages 319-333.
    2. Adrian W. Bowman & Marco Giannitrapani & E. Marian Scott, 2009. "Spatiotemporal smoothing and sulphur dioxide trends over Europe," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 737-752, December.
    3. Yongsheng Jiang & Dong Zhao & Andrew Sanderford & Jing Du, 2018. "Effects of Bank Lending on Urban Housing Prices for Sustainable Development: A Panel Analysis of Chinese Cities," Sustainability, MDPI, vol. 10(3), pages 1-16, February.
    4. Jun Jin & Tiefeng Ma & Jiajia Dai, 2021. "New efficient spline estimation for varying-coefficient models with two-step knot number selection," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 693-712, July.

    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. Zhao, Yan-Yong & Lin, Jin-Guan & Xu, Pei-Rong & Ye, Xu-Guo, 2015. "Orthogonality-projection-based estimation for semi-varying coefficient models with heteroscedastic errors," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 204-221.
    2. Zhang, Ting, 2015. "Semiparametric model building for regression models with time-varying parameters," Journal of Econometrics, Elsevier, vol. 187(1), pages 189-200.
    3. Zhang, Wenyang & Li, Degui & Xia, Yingcun, 2015. "Estimation in generalised varying-coefficient models with unspecified link functions," Journal of Econometrics, Elsevier, vol. 187(1), pages 238-255.
    4. Jing Sun & Lu Lin, 2014. "Local rank estimation and related test for varying-coefficient partially linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 187-206, March.
    5. Ngai Hang Chan & Linhao Gao & Wilfredo Palma, 2022. "Simultaneous variable selection and structural identification for time‐varying coefficient models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 511-531, July.
    6. Liang, Zhongwen & Li, Qi, 2012. "Functional coefficient regression models with time trend," Journal of Econometrics, Elsevier, vol. 170(1), pages 15-31.
    7. Karmakar, Sayar & Richter, Stefan & Wu, Wei Biao, 2022. "Simultaneous inference for time-varying models," Journal of Econometrics, Elsevier, vol. 227(2), pages 408-428.
    8. Zhao, Weihua & Zhang, Riquan & Liu, Jicai & Hu, Hongchang, 2015. "Robust adaptive estimation for semivarying coefficient models," Statistics & Probability Letters, Elsevier, vol. 97(C), pages 132-141.
    9. Jialiang Li & Wenyang Zhang & Zhengxiao Wu, 2011. "Optimal zone for bandwidth selection in semiparametric models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(3), pages 701-717.
    10. Zhao, Yan-Yong & Lin, Jin-Guan & Huang, Xing-Fang & Wang, Hong-Xia, 2016. "Adaptive jump-preserving estimates in varying-coefficient models," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 65-80.
    11. Weihua Zhao & Riquan Zhang & Jicai Liu & Yazhao Lv, 2014. "Robust and efficient variable selection for semiparametric partially linear varying coefficient model based on modal regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(1), pages 165-191, February.
    12. Zhou Zhou & Wei Biao Wu, 2010. "Simultaneous inference of linear models with time varying coefficients," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 513-531, September.
    13. Degui Li & Jia Chen & Zhengyan Lin, 2009. "Variable selection in partially time-varying coefficient models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(5), pages 553-566.
    14. Wang, Qihua & Xue, Liugen, 2011. "Statistical inference in partially-varying-coefficient single-index model," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 1-19, January.
    15. Alan T. K. Wan & Shangyu Xie & Yong Zhou, 2017. "A varying coefficient approach to estimating hedonic housing price functions and their quantiles," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 1979-1999, August.
    16. Yan Sun & Jialiang Li & Wenyang Zhang, 2012. "Estimation and model selection in a class of semiparametric models for cluster data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(4), pages 835-856, August.
    17. Peng, Heng & Xie, Chuanlong & Zhao, Jingxin, 2021. "Fast inference for semi-varying coefficient models via local averaging," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    18. Lujia Bai & Weichi Wu, 2021. "Detecting long-range dependence for time-varying linear models," Papers 2110.08089, arXiv.org, revised Mar 2023.
    19. Hu, Lixia & Huang, Tao & You, Jinhong, 2019. "Two-step estimation of time-varying additive model for locally stationary time series," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 94-110.
    20. Bonsoo Koo & Oliver Linton, 2010. "Semiparametric Estimation of Locally Stationary Diffusion Models," STICERD - Econometrics Paper Series 551, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

    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:jorssa:v:170:y:2007:i:3:p:691-711. 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.