[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

The Bayesian conditional independence model for measurement error: applications in ecology

  • Published:
Environmental and Ecological Statistics Aims and scope Submit manuscript

Abstract

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Adcock RJ (1877) Annote on the method of least squares. Analyst 4: 183–184

    Article  Google Scholar 

  • Adcock RJ (1878) A problem in least squares. Analyst 5: 53–54

    Article  Google Scholar 

  • Austin MP, Meyers JA (1996) Current approaches to modelling the environmental niche of eucalypts—implications for management of forest biodiversity. For Ecol Manag 85: 95–106

    Article  Google Scholar 

  • Bashir SA, Duffy SW (1997) The correction of risk estimates for measurement error. Ann Epidemiol 7: 154–164

    Article  PubMed  CAS  Google Scholar 

  • Beamish RJ, McFarlane GA (1983) The forgotten requirement for age validation in fisheries biology. Trans Am Fish Soc 112(6): 735–743

    Article  Google Scholar 

  • Berkson J (1950) Are there two regressions?. J Amer Stat Assoc 45: 164–180

    Article  Google Scholar 

  • Campana SE (2001) Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods. J Fish Biol 59: 197–242

    Article  Google Scholar 

  • Carroll RJ, Ruppert D, Stefanski LA (1995) Measurement error in nonlinear models. Chapman and Hall, New York

    Google Scholar 

  • Cheng CL, Ness JWV (1999) Statistical regression with measurement error. Kendall’s Library of Statistics, Arnold

    Google Scholar 

  • Clayton DG (1992) Models for the analysis of cohort and case-control studies with inaccurately measured exposures. In: Statistical models for longitudinal studies of health

  • Dellaportas P, Stephens DA (1995) Bayesian analysis of errors–in–variables regression models. Biometrics 51: 1085–1095

    Article  Google Scholar 

  • Fernandes R, Leblanc SG (2005) Parametric (modified least squares) and non-parametric (Theil-Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors. Remote Sens Environ 95(3): 303–316

    Article  Google Scholar 

  • Fuller WA (1987) Measurement error models. Wiley series in probability and mathematical statistics. Applied probability and statistics. Wiley, New York

    Google Scholar 

  • Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4): 457–511

    Article  Google Scholar 

  • Gustafson P (2003) Measurement error and misclassification in statistics and epidemology: impacts and Bayesian adjustments. Chapman & Hall/CRC, London

    Book  Google Scholar 

  • Hutchinson MF, Nix HA, Houlder DJ, McMahon PJ (1998) ANUCLIM version 1.6 user guide. Centre for resource and environmental studies. The Australian National University, Canberra

    Google Scholar 

  • Kuha J (1997) Estimation by data augmentation in regression models with continuous and discrete covariates measured with error. Stat Med 16: 189–202

    Article  PubMed  CAS  Google Scholar 

  • Mallick BK, Gelfand AE (1996) Semiparametric errors-in-variables models. A Bayesian approach. J Stat Plann Infer 52: 307–321

    Article  Google Scholar 

  • Müller P, Roeder K (1997) A Bayesian semiparametric model for case-control studies with errors in variables. Biometrika 84: 523–537

    Article  Google Scholar 

  • Phillips AN, Smith GD (1992) Bias in relative odds estimation owing to imprecise measurement of correlated exposures. Stat Med 11: 953–961

    Article  PubMed  CAS  Google Scholar 

  • Richardson S, Gilks WR (1993) A Bayesian approach to measurement error problems in epidemiology using conditional independence models. Am J Epidemiol 138(6): 430–442

    PubMed  CAS  Google Scholar 

  • Richardson S, Gilks WR (1993) Conditional independence models for epidemiological studies with covariate measurement error. Stat Med 12: 1703–1722

    Article  PubMed  CAS  Google Scholar 

  • Spiegelhalter D, Thomas A, Best NG (1996) BUGS: Bayesian inference using Gibbs sampling. Medical Research Council Biostatistics Unit, Cambridge

    Google Scholar 

  • Williams K, Norman P, Mengersen K (2000) Predicting the natural occurence of blackbutt and gympie messmate in southeast queensland. Aust For 63(3): 199–210

    Google Scholar 

  • Yuan LL (2007) Effects of measurement error on inferences of environmental conditions. J N Am Benthol Soc 26(1): 152–163

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Queensland Department of Environment and Resource Management, Brisbane, Australia

    Robert J. Denham

  2. School of Mathematical Sciences, Queensland University of Technology, 2 George Street, Brisbane, 4000, Australia

    Matthew G. Falk & Kerrie L. Mengersen

Authors
  1. Robert J. Denham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew G. Falk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kerrie L. Mengersen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthew G. Falk.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Denham, R.J., Falk, M.G. & Mengersen, K.L. The Bayesian conditional independence model for measurement error: applications in ecology. Environ Ecol Stat 18, 239–255 (2011). https://doi.org/10.1007/s10651-009-0130-3

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10651-009-0130-3

Keywords

Search

Navigation