IDEAS home Printed from https://ideas.repec.org/a/spr/alstar/v101y2017i4d10.1007_s10182-017-0304-5.html
   My bibliography  Save this article

Variance estimation for integrated population models

Author

Listed:
  • Panagiotis Besbeas

    (Athens University of Economics and Business
    University of Kent)

  • Byron J. T. Morgan

    (University of Kent)

Abstract

State-space models are widely used in ecology. However, it is well known that in practice it can be difficult to estimate both the process and observation variances that occur in such models. We consider this issue for integrated population models, which incorporate state-space models for population dynamics. To some extent, the mechanism of integrated population models protects against this problem, but it can still arise, and two illustrations are provided, in each of which the observation variance is estimated as zero. In the context of an extended case study involving data on British Grey herons, we consider alternative approaches for dealing with the problem when it occurs. In particular, we consider penalised likelihood, a method based on fitting splines and a method of pseudo-replication, which is undertaken via a simple bootstrap procedure. For the case study of the paper, it is shown that when it occurs, an estimate of zero observation variance is unimportant for inference relating to the model parameters of primary interest. This unexpected finding is supported by a simulation study.

Suggested Citation

  • Panagiotis Besbeas & Byron J. T. Morgan, 2017. "Variance estimation for integrated population models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 439-460, October.
    • Handle: RePEc:spr:alstar:v:101:y:2017:i:4:d:10.1007_s10182-017-0304-5
    DOI: 10.1007/s10182-017-0304-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10182-017-0304-5
    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/s10182-017-0304-5?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. S. C. Barry & S. P. Brooks & E. A. Catchpole & B. J. T. Morgan, 2003. "The Analysis of Ring-Recovery Data Using Random Effects," Biometrics, The International Biometric Society, vol. 59(1), pages 54-65, March.
    2. Toby A. Patterson & Alison Parton & Roland Langrock & Paul G. Blackwell & Len Thomas & Ruth King, 2017. "Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 399-438, October.
    3. P. Besbeas & J.‐D. Lebreton & B. J. T. Morgan, 2003. "The efficient integration of abundance and demographic data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 95-102, January.
    4. Wang, Ji-Ping Z. & Lindsay, Bruce G., 2005. "A Penalized Nonparametric Maximum Likelihood Approach to Species Richness Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 942-959, September.
    5. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    6. P. Besbeas & S. N. Freeman & B. J. T. Morgan & E. A. Catchpole, 2002. "Integrating Mark–Recapture–Recovery and Census Data to Estimate Animal Abundance and Demographic Parameters," Biometrics, The International Biometric Society, vol. 58(3), pages 540-547, September.
    7. Bengtsson, Thomas & Cavanaugh, Joseph E., 2006. "An improved Akaike information criterion for state-space model selection," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2635-2654, June.
    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. Chen, Simiao & Prettner, Klaus & Kuhn, Michael & Bloom, David E., 2021. "The economic burden of COVID-19 in the United States: Estimates and projections under an infection-based herd immunity approach," The Journal of the Economics of Ageing, Elsevier, vol. 20(C).
    2. Roland Langrock & David L. Borchers, 2017. "Guest editors’ introduction to the special issue on “Ecological Statistics”," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 345-347, October.
    3. Simone Tenan & Paolo Pedrini & Natalia Bragalanti & Claudio Groff & Chris Sutherland, 2017. "Data integration for inference about spatial processes: A model-based approach to test and account for data inconsistency," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-18, October.
    4. Sharifi, Farahnaz & Nygaard, Andi & Stone, Wendy M. & Levin, Iris, 2021. "Green gentrification or gentrified greening: Metropolitan Melbourne," Land Use Policy, Elsevier, vol. 108(C).
    5. Besbeas, P.T. & McCrea, R.S. & Morgan, B.J.T., 2022. "Selecting age structure in integrated population models," Ecological Modelling, Elsevier, vol. 473(C).
    6. Lemmons, Brianna P. & Bamishigbin, Olajide N. & Cryer-Coupet, Qiana R. & Lewis, Ericka M. & Dorsey, Marquitta S. & Johnson, Shawnice, 2021. "Exploring the determinants of discipline and monitoring among non-resident African American fathers," Children and Youth Services Review, Elsevier, vol. 125(C).

    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. Besbeas, P.T. & McCrea, R.S. & Morgan, B.J.T., 2022. "Selecting age structure in integrated population models," Ecological Modelling, Elsevier, vol. 473(C).
    2. Nichols, J.M. & Spendelow, J.A. & Nichols, J.D., 2017. "Using Optimal Transport Theory to Estimate Transition Probabilities in Metapopulation Dynamics," Ecological Modelling, Elsevier, vol. 359(C), pages 311-319.
    3. Mosnier, A. & Doniol-Valcroze, T. & Gosselin, J.-F. & Lesage, V. & Measures, L.N. & Hammill, M.O., 2015. "Insights into processes of population decline using an integrated population model: The case of the St. Lawrence Estuary beluga (Delphinapterus leucas)," Ecological Modelling, Elsevier, vol. 314(C), pages 15-31.
    4. R. B. O'Hara & S. Lampila & M. Orell, 2009. "Estimation of Rates of Births, Deaths, and Immigration from Mark–Recapture Data," Biometrics, The International Biometric Society, vol. 65(1), pages 275-281, March.
    5. Paul B. Conn & Duane R. Diefenbach & Jeffrey L. Laake & Mark A. Ternent & Gary C. White, 2008. "Bayesian Analysis of Wildlife Age-at-Harvest Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1170-1177, December.
    6. Dennis, Emily B. & Kéry, Marc & Morgan, Byron J.T. & Coray, Armin & Schaub, Michael & Baur, Bruno, 2021. "Integrated modelling of insect population dynamics at two temporal scales," Ecological Modelling, Elsevier, vol. 441(C).
    7. Gimenez, Olivier & Rossi, Vivien & Choquet, Rémi & Dehais, Camille & Doris, Blaise & Varella, Hubert & Vila, Jean-Pierre & Pradel, Roger, 2007. "State-space modelling of data on marked individuals," Ecological Modelling, Elsevier, vol. 206(3), pages 431-438.
    8. Ruth King & Stephen P. Brooks & Chiara Mazzetta & Stephen N. Freeman & Byron J. T. Morgan, 2008. "Identifying and diagnosing population declines: a Bayesian assessment of lapwings in the UK," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(5), pages 609-632, December.
    9. Avanzi, Benjamin & Taylor, Greg & Vu, Phuong Anh & Wong, Bernard, 2020. "A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 50-71.
    10. François R. Velde, 2009. "Chronicle of a Deflation Unforetold," Journal of Political Economy, University of Chicago Press, vol. 117(4), pages 591-634, August.
    11. Wen Xu, 2016. "Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters," Econometrics, MDPI, vol. 4(4), pages 1, October.
    12. Alejandro Rodriguez & Esther Ruiz, 2009. "Bootstrap prediction intervals in state–space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 167-178, March.
    13. Parrini, Alessandro, 2013. "Importance Sampling for Portfolio Credit Risk in Factor Copula Models," MPRA Paper 103745, University Library of Munich, Germany.
    14. Jean-Luc Gaffard, 2014. "Crise de la théorie et crise de la politique économique. Des modèles d'équilibre général stochastique aux modèles de dynamique hors de l'équilibre," Revue économique, Presses de Sciences-Po, vol. 65(1), pages 71-96.
    15. Salman Huseynov, 2021. "Long and short memory in dynamic term structure models," CREATES Research Papers 2021-15, Department of Economics and Business Economics, Aarhus University.
    16. Tsionas, Mike G., 2021. "Bayesian forecasting with the structural damped trend model," International Journal of Production Economics, Elsevier, vol. 234(C).
    17. Tommaso Proietti, 2002. "Some Reflections on Trend-Cycle Decompositions with Correlated Components," Econometrics 0209002, University Library of Munich, Germany.
    18. Tobias Hartl & Roland Jucknewitz, 2022. "Approximate state space modelling of unobserved fractional components," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 75-98, January.
    19. Broto Carmen & Ruiz Esther, 2009. "Testing for Conditional Heteroscedasticity in the Components of Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-30, May.
    20. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.

    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:alstar:v:101:y:2017:i:4:d:10.1007_s10182-017-0304-5. 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.