Assessing the ability of adaptive designs to capture trends in hard coral cover
Author
Abstract
Suggested Citation
DOI: 10.1002/env.2802
Download full text from publisher
References listed on IDEAS
- Alan R Pearse & James M McGree & Nicholas A Som & Catherine Leigh & Paul Maxwell & Jay M Ver Hoef & Erin E Peterson, 2020. "SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-17, September.
- N. Friel & A. N. Pettitt, 2008. "Marginal likelihood estimation via power posteriors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 589-607, July.
- David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Linde, 2014. "The deviance information criterion: 12 years on," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 485-493, June.
- Oedekoven, C.S. & King, R. & Buckland, S.T. & Mackenzie, M.L. & Evans, K.O. & Burger, L.W., 2016. "Using hierarchical centering to facilitate a reversible jump MCMC algorithm for random effects models," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 79-90.
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.- Gill Rowlands & David Whitney & Graham Moon, 2018. "Developing and Applying Geographical Synthetic Estimates of Health Literacy in GP Clinical Systems," IJERPH, MDPI, vol. 15(8), pages 1-8, August.
- Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
- Jeong Eun Lee & Christian Robert, 2013. "Imortance Sampling Schemes for Evidence Approximation in Mixture Models," Working Papers 2013-42, Center for Research in Economics and Statistics.
- Kai Yang & Qingqing Zhang & Xinyang Yu & Xiaogang Dong, 2023. "Bayesian inference for a mixture double autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 188-207, May.
- Kelvyn Jones & David Manley & Ron Johnston & Dewi Owen, 2018. "Modelling residential segregation as unevenness and clustering: A multilevel modelling approach incorporating spatial dependence and tackling the MAUP," Environment and Planning B, , vol. 45(6), pages 1122-1141, November.
- Mutz, Rüdiger & Daniel, Hans-Dieter, 2018. "The bibliometric quotient (BQ), or how to measure a researcher’s performance capacity: A Bayesian Poisson Rasch model," Journal of Informetrics, Elsevier, vol. 12(4), pages 1282-1295.
- Will Penny & Biswa Sengupta, 2016. "Annealed Importance Sampling for Neural Mass Models," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-25, March.
- Spezia, L. & Cooksley, S.L. & Brewer, M.J. & Donnelly, D. & Tree, A., 2014. "Modelling species abundance in a river by Negative Binomial hidden Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 599-614.
- Papastamoulis, Panagiotis, 2018. "Overfitting Bayesian mixtures of factor analyzers with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 220-234.
- Han, Shengtong & Zhang, Hongmei & Karmaus, Wilfried & Roberts, Graham & Arshad, Hasan, 2017. "Adjusting background noise in cluster analyses of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 93-104.
- Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
- Jihye Kim & Wendy Olsen & Arkadiusz Wiśniowski, 2020. "A Bayesian Estimation of Child Labour in India," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 13(6), pages 1975-2001, December.
- Spezia, Luigi, 2020. "Bayesian variable selection in non-homogeneous hidden Markov models through an evolutionary Monte Carlo method," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
- Schubert, Anna-Lena & Nunez, Michael D. & Hagemann, Dirk & Vandekerckhove, Joachim, 2018. "Individual differences in cortical processing speed predict cognitive abilities: A model-based cognitive neuroscience account," OSF Preprints yfa8s, Center for Open Science.
- Shuhui Guo & Lihua Xiong & Jie Chen & Shenglian Guo & Jun Xia & Ling Zeng & Chong-Yu Xu, 2023. "Nonstationary Regional Flood Frequency Analysis Based on the Bayesian Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 659-681, January.
- Elaine A. Ferguson & Jason Matthiopoulos & Robert H. Insall & Dirk Husmeier, 2017. "Statistical inference of the mechanisms driving collective cell movement," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 869-890, August.
- Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022.
"An automated prior robustness analysis in Bayesian model comparison,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
- Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2019. "An automated prior robustness analysis in Bayesian model comparison," CAMA Working Papers 2019-45, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Luigi Spezia & Andy Vinten & Roberta Paroli & Marc Stutter, 2021. "An evolutionary Monte Carlo method for the analysis of turbidity high‐frequency time series through Markov switching autoregressive models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
- Hazelton, Martin L. & Parry, Katharina, 2016. "Statistical methods for comparison of day-to-day traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 22-34.
- Muhammed Semakula & Franco̧is Niragire & Christel Faes, 2020. "Bayesian spatio-temporal modeling of malaria risk in Rwanda," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
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:wly:envmet:v:34:y:2023:i:6:n:e2802. 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: http://www.interscience.wiley.com/jpages/1180-4009/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.