Measuring diachronic sense change: New models and Monte Carlo methods for Bayesian inference
Author
Abstract
Suggested Citation
DOI: 10.1111/rssc.12591
Download full text from publisher
References listed on IDEAS
- Gareth O. Roberts & Jeffrey S. Rosenthal, 1998. "Optimal scaling of discrete approximations to Langevin diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 255-268.
- Groenewald, Pieter C. N. & Mokgatlhe, Lucky, 2005. "Bayesian computation for logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 857-868, April.
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.- Jorge I. Figueroa-Zúñiga & Cristian L. Bayes & Víctor Leiva & Shuangzhe Liu, 2022. "Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications," Statistical Papers, Springer, vol. 63(3), pages 919-942, June.
- Mamatzakis, Emmanuel C. & Tsionas, Mike G., 2021. "Making inference of British household's happiness efficiency: A Bayesian latent model," European Journal of Operational Research, Elsevier, vol. 294(1), pages 312-326.
- Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
- Delis, Manthos D. & Tsionas, Mike G., 2018. "Measuring management practices," International Journal of Production Economics, Elsevier, vol. 199(C), pages 65-77.
- Dalalyan, Arnak S. & Karagulyan, Avetik, 2019.
"User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient,"
Stochastic Processes and their Applications, Elsevier, vol. 129(12), pages 5278-5311.
- Arnak Dalalyan & Avetik Karagulyan, 2017. "User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient," Working Papers 2017-20, Center for Research in Economics and Statistics.
- Aknouche, Abdelhakim & Dimitrakopoulos, Stefanos, 2020. "On an integer-valued stochastic intensity model for time series of counts," MPRA Paper 105406, University Library of Munich, Germany.
- Arnak S. Dalalyan, 2017.
"Theoretical guarantees for approximate sampling from smooth and log-concave densities,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 651-676, June.
- Arnak S. Dalalyan, 2014. "Theoretical guarantees for approximate sampling from smooth and log-concave densities," Working Papers 2014-45, Center for Research in Economics and Statistics.
- O. F. Christensen & J. Møller & R. P. Waagepetersen, 2001. "Geometric Ergodicity of Metropolis-Hastings Algorithms for Conditional Simulation in Generalized Linear Mixed Models," Methodology and Computing in Applied Probability, Springer, vol. 3(3), pages 309-327, September.
- M Ludkin & C Sherlock, 2023. "Hug and hop: a discrete-time, nonreversible Markov chain Monte Carlo algorithm," Biometrika, Biometrika Trust, vol. 110(2), pages 301-318.
- Tsionas, Mike G. & Michaelides, Panayotis G., 2017.
"Neglected chaos in international stock markets: Bayesian analysis of the joint return–volatility dynamical system,"
Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 95-107.
- Tsionas, Mike G. & Michaelides, Panayotis G., 2017. "Neglected chaos in international stock markets: Bayesian analysis of the joint return–volatility dynamical system," LSE Research Online Documents on Economics 80749, London School of Economics and Political Science, LSE Library.
- G. O. Roberts & O. Stramer, 2002. "Langevin Diffusions and Metropolis-Hastings Algorithms," Methodology and Computing in Applied Probability, Springer, vol. 4(4), pages 337-357, December.
- Shao, Wei & Guo, Guangbao & Meng, Fanyu & Jia, Shuqin, 2013. "An efficient proposal distribution for Metropolis–Hastings using a B-splines technique," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 465-478.
- Anandamayee Majumdar & Corinna Gries & Jason Walker, 2011. "A non-stationary spatial generalized linear mixed model approach for studying plant diversity," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1935-1950, October.
- Reihaneh Entezari & Patrick E. Brown & Jeffrey S. Rosenthal, 2020. "Bayesian spatial analysis of hardwood tree counts in forests via MCMC," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
- N. Englezos & X. Kartala & P. Koundouri & M. Tsionas & A. Alamanos, 2023.
"A Novel HydroEconomic - Econometric Approach for Integrated Transboundary Water Management Under Uncertainty,"
Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 975-1030, April.
- Nikolaos Englezos & Xanthi Kartala & Phoebe Koundouri & Mike Tsionas & Angelos Alamanos, 2021. "A Novel Hydro - Economic - Econometric Approach for Integrated Transboundary Water Management under Uncertainty," DEOS Working Papers 2101, Athens University of Economics and Business.
- Moffa, Giusi & Kuipers, Jack, 2014. "Sequential Monte Carlo EM for multivariate probit models," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 252-272.
- Lambert, Philippe & Eilers, Paul H.C., 2009. "Bayesian density estimation from grouped continuous data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1388-1399, February.
- Peter Neal & Gareth Roberts, 2008. "Optimal Scaling for Random Walk Metropolis on Spherically Constrained Target Densities," Methodology and Computing in Applied Probability, Springer, vol. 10(2), pages 277-297, June.
- Palczewski, Andrzej & Palczewski, Jan, 2019. "Black–Litterman model for continuous distributions," European Journal of Operational Research, Elsevier, vol. 273(2), pages 708-720.
- Burda Martin & Maheu John M., 2013.
"Bayesian adaptively updated Hamiltonian Monte Carlo with an application to high-dimensional BEKK GARCH models,"
Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 345-372, September.
- Martin Burda & John M. Maheu, 2012. "Bayesian Adaptively Updated Hamiltonian Monte Carlo with an Application to High-Dimensional BEKK GARCH Models," Working Paper series 46_12, Rimini Centre for Economic Analysis.
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:71:y:2022:i:5:p:1569-1604. 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.