My authors
Follow this author
Omiros Papaspiliopoulos
Citations
Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.Articles
- C. Yau & O. Papaspiliopoulos & G. O. Roberts & C. Holmes, 2011.
"Bayesian non‐parametric hidden Markov models with applications in genomics,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 37-57, January.
Cited by:
- Laura Liu, 2020.
"Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective,"
CAEPR Working Papers
2020-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
- Laura Liu, 2018. "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," Papers 1805.04178, arXiv.org, revised Oct 2021.
- Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (U.S.).
- Bartolucci, Francesco & Farcomeni, Alessio & Pennoni, Fulvia, 2012.
"Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates,"
MPRA Paper
39023, University Library of Munich, Germany.
- F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
- Chopin, Nicolas & Gadat, Sébastien & Guedj, Benjamin & Guyader, Arnaud & Vernet, Elodie, 2015. "On some recent advances in high dimensional Bayesian Statistics," TSE Working Papers 15-557, Toulouse School of Economics (TSE).
- Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "On the stick–breaking representation of normalized inverse Gaussian priors," DEM Working Papers Series 008, University of Pavia, Department of Economics and Management.
- Zheng, Jing & Yu, Dongjie & Zhu, Bin & Tong, Changqing, 2022. "Learning hidden Markov models with unknown number of states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
- Richard L. Warr & Travis B. Woodfield, 2020. "Bayesian nonparametric estimation of first passage distributions in semi‐Markov processes," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(2), pages 237-250, March.
- Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013.
"Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models,"
International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
- Jason Ng & Catherine S. Forbes & Gael M. Martin & Brendan P.M. McCabe, 2011. "Non-Parametric Estimation of Forecast Distributions in Non-Gaussian, Non-linear State Space Models," Monash Econometrics and Business Statistics Working Papers 11/11, Monash University, Department of Econometrics and Business Statistics.
- Adam Persin & Ajay Jasr, 2016. "Twisting the Alive Particle Filter," Methodology and Computing in Applied Probability, Springer, vol. 18(2), pages 335-358, June.
- Raffaele Argiento & Matteo Ruggiero, 2018. "Computational challenges and temporal dependence in Bayesian nonparametric models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 231-238, June.
- Wang, Jiangzhou & Cui, Tingting & Zhu, Wensheng & Wang, Pengfei, 2023. "Covariate-modulated large-scale multiple testing under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
- Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
- Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
- Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
- Xia, Ye-Mao & Tang, Nian-Sheng, 2019. "Bayesian analysis for mixture of latent variable hidden Markov models with multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 190-211.
- Laura Liu, 2020.
"Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective,"
CAEPR Working Papers
2020-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
- Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts, 2008.
"Particle filters for partially observed diffusions,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 755-777, September.
Cited by:
- Mark Briers & Arnaud Doucet & Simon Maskell, 2010. "Smoothing algorithms for state–space models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 61-89, February.
- Alexandros Beskos & Omiros Papaspiliopoulos & Gareth O. Roberts & Paul Fearnhead, 2006. "Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 333-382, June.
- James Hodgson & Adam M. Johansen & Murray Pollock, 2022. "Unbiased Simulation of Rare Events in Continuous Time," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 2123-2148, September.
- Shoji, Isao, 2013. "Filtering for partially observed diffusion and its applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 4966-4976.
- Kalogeropoulos, Konstantinos & Roberts, Gareth O. & Dellaportas, Petros, 2010.
"Inference for stochastic volatility models using time change transformations,"
LSE Research Online Documents on Economics
31421, London School of Economics and Political Science, LSE Library.
- Kalogeropoulos, Konstantinos & Roberts, Gareth O. & Dellaportas, Petros, 2007. "Inference for stochastic volatility model using time change transformations," MPRA Paper 5697, University Library of Munich, Germany.
- Konstantinos Kalogeropoulos & Gareth O. Roberts & Petros Dellaportas, 2007. "Inference for stochastic volatility models using time change transformations," Papers 0711.1594, arXiv.org.
- Crucinio, Francesca R. & Johansen, Adam M., 2023. "Properties of marginal sequential Monte Carlo methods," Statistics & Probability Letters, Elsevier, vol. 203(C).
- Johansen, Adam M. & Doucet, Arnaud, 2008. "A note on auxiliary particle filters," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1498-1504, September.
- Jourdain Benjamin & Sbai Mohamed, 2007. "Exact retrospective Monte Carlo computation of arithmetic average Asian options," Monte Carlo Methods and Applications, De Gruyter, vol. 13(2), pages 135-171, July.
- Ajay Jasra & Kody Law & Carina Suciu, 2020. "Advanced Multilevel Monte Carlo Methods," International Statistical Review, International Statistical Institute, vol. 88(3), pages 548-579, December.
- 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.
- Stefano Iacus & Masayuki Uchida & Nakahiro Yoshida, 2006.
"Parametric estimation for partially hidden diffusion processes sampled at discrete times,"
UNIMI - Research Papers in Economics, Business, and Statistics
unimi-1042, Universitá degli Studi di Milano.
- Iacus, Stefano Maria & Uchida, Masayuki & Yoshida, Nakahiro, 2009. "Parametric estimation for partially hidden diffusion processes sampled at discrete times," Stochastic Processes and their Applications, Elsevier, vol. 119(5), pages 1580-1600, May.
- Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts & Andrew Stuart, 2010. "Random‐weight particle filtering of continuous time processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 497-512, September.
- N. Chopin & P. E. Jacob & O. Papaspiliopoulos, 2013. "SMC-super-2: an efficient algorithm for sequential analysis of state space models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 397-426, June.
- Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
- Hermann Singer, 2011. "Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 375-413, December.
- Panayotis Michaelides & Mike Tsionas & Panos Xidonas, 2020. "A Bayesian Signals Approach for the Detection of Crises," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 551-585, September.
- Murray, Lawrence M., 2015. "Bayesian State-Space Modelling on High-Performance Hardware Using LibBi," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i10).
- Omiros Papaspiliopoulos & Gareth O. Roberts, 2008.
"Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models,"
Biometrika, Biometrika Trust, vol. 95(1), pages 169-186.
Cited by:
- Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
- Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2016.
"A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection,"
Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 814-829.
- Audrone Virbickaite & M. Concepci'on Aus'in & Pedro Galeano, 2013. "A Bayesian Non-Parametric Approach to Asymmetric Dynamic Conditional Correlation Model With Application to Portfolio Selection," Papers 1301.5129, arXiv.org, revised Jan 2014.
- Laura Liu, 2020.
"Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective,"
CAEPR Working Papers
2020-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
- Laura Liu, 2018. "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," Papers 1805.04178, arXiv.org, revised Oct 2021.
- Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (U.S.).
- Mahdi Hosseinpouri & Majid Jafari Khaledi, 2019. "An area-specific stick breaking process for spatial data," Statistical Papers, Springer, vol. 60(1), pages 199-221, February.
- Jim E. Griffin & Fabrizio Leisen, 2017. "Compound random measures and their use in Bayesian non-parametrics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 525-545, March.
- Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019.
"Bayesian nonparametric sparse VAR models,"
Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
- Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.
- Ausín, M. Concepción & Galeano, Pedro & Ghosh, Pulak, 2014.
"A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation,"
European Journal of Operational Research, Elsevier, vol. 232(2), pages 350-358.
- Galeano, Pedro & Ghosh, Pulak, 2010. "A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation," DES - Working Papers. Statistics and Econometrics. WS ws103822, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Kalogeropoulos, Konstantinos & Roberts, Gareth O. & Dellaportas, Petros, 2010.
"Inference for stochastic volatility models using time change transformations,"
LSE Research Online Documents on Economics
31421, London School of Economics and Political Science, LSE Library.
- Kalogeropoulos, Konstantinos & Roberts, Gareth O. & Dellaportas, Petros, 2007. "Inference for stochastic volatility model using time change transformations," MPRA Paper 5697, University Library of Munich, Germany.
- Konstantinos Kalogeropoulos & Gareth O. Roberts & Petros Dellaportas, 2007. "Inference for stochastic volatility models using time change transformations," Papers 0711.1594, arXiv.org.
- Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "On the stick–breaking representation of normalized inverse Gaussian priors," DEM Working Papers Series 008, University of Pavia, Department of Economics and Management.
- Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2018.
"Bayesian Nonparametric Calibration and Combination of Predictive Distributions,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 675-685, April.
- Roberto Casarin & Federico Bassetti & Francesco Ravazzolo, 2015. "Bayesian Nonparametric Calibration and Combination of Predictive Distributions," Working Papers 2015:04, Department of Economics, University of Venice "Ca' Foscari".
- Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2015. "Bayesian nonparametric calibration and combination of predictive distributions," Working Paper 2015/03, Norges Bank.
- Richard F. MacLehose & David B. Dunson, 2010. "Bayesian Semiparametric Multiple Shrinkage," Biometrics, The International Biometric Society, vol. 66(2), pages 455-462, June.
- Sun Jiehuan & Warren Joshua L. & Zhao Hongyu, 2017. "A Bayesian semiparametric factor analysis model for subtype identification," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 145-158, April.
- Lancelot F. James & Antonio Lijoi & Igor Prünster, 2009. "Posterior Analysis for Normalized Random Measures with Independent Increments," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 76-97, March.
- Pati, Debdeep & Dunson, David B. & Tokdar, Surya T., 2013. "Posterior consistency in conditional distribution estimation," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 456-472.
- Pelenis, Justinas, 2014. "Bayesian regression with heteroscedastic error density and parametric mean function," Journal of Econometrics, Elsevier, vol. 178(P3), pages 624-638.
- Crespo Cuaresma, Jesus & Grün, Bettina & Hofmarcher, Paul & Humer, Stefan & Moser, Mathias, 2016. "Unveiling covariate inclusion structures in economic growth regressions using latent class analysis," European Economic Review, Elsevier, vol. 81(C), pages 189-202.
- Miller, Jeffrey W., 2019. "An elementary derivation of the Chinese restaurant process from Sethuraman’s stick-breaking process," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 112-117.
- Yang, Mingan, 2012. "Bayesian variable selection for logistic mixed model with nonparametric random effects," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2663-2674.
- Debdeep Pati & David Dunson, 2014. "Bayesian nonparametric regression with varying residual density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(1), pages 1-31, February.
- Stefano Tonellato, 2019. "Bayesian nonparametric clustering as a community detection problem," Working Papers 2019: 20, Department of Economics, University of Venice "Ca' Foscari".
- Luis E. Nieto-Barajas & Peter Müller & Yuan Ji & Yiling Lu & Gordon B. Mills, 2012. "A Time-Series DDP for Functional Proteomics Profiles," Biometrics, The International Biometric Society, vol. 68(3), pages 859-868, September.
- Pelenis, Justinas, 2012. "Bayesian Semiparametric Regression," Economics Series 285, Institute for Advanced Studies.
- Zhang, Junyi & Dassios, Angelos, 2023. "Truncated two-parameter Poisson-Dirichlet approximation for Pitman-Yor process hierarchical models," LSE Research Online Documents on Economics 120294, London School of Economics and Political Science, LSE Library.
- Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
- Cai, Bo & Meyer, Renate, 2011. "Bayesian semiparametric modeling of survival data based on mixtures of B-spline distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1260-1272, March.
- Im, Yunju & Tan, Aixin, 2021. "Bayesian subgroup analysis in regression using mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
- Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse seemingly unrelated regression model (SUR)," Working Papers 2016:20, Department of Economics, University of Venice "Ca' Foscari".
- Junyi Zhang & Angelos Dassios, 2024. "Truncated two‐parameter Poisson–Dirichlet approximation for Pitman–Yor process hierarchical models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 590-611, June.
- Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
- De Blasi, Pierpaolo & Martínez, Asael Fabian & Mena, Ramsés H. & Prünster, Igor, 2020. "On the inferential implications of decreasing weight structures in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
- Sylvia Frühwirth-Schnatter & Gertraud Malsiner-Walli, 2019. "From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 33-64, March.
- Isadora Antoniano-Villalobos & Stephen G. Walker, 2016. "A Nonparametric Model for Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 126-142, January.
- Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
- Li, Mingyang & Meng, Hongdao & Zhang, Qingpeng, 2017. "A nonparametric Bayesian modeling approach for heterogeneous lifetime data with covariates," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 95-104.
- Ungolo, Francesco & van den Heuvel, Edwin R., 2024. "A Dirichlet process mixture regression model for the analysis of competing risk events," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 95-113.
- Rebecca Graziani & Michele Guindani & Peter F. Thall, 2015. "Bayesian nonparametric estimation of targeted agent effects on biomarker change to predict clinical outcome," Biometrics, The International Biometric Society, vol. 71(1), pages 188-197, March.
- C. Yau & O. Papaspiliopoulos & G. O. Roberts & C. Holmes, 2011. "Bayesian non‐parametric hidden Markov models with applications in genomics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 37-57, January.
- Moya, Blake & Walker, Stephen G., 2024. "Full uncertainty analysis for Bayesian nonparametric mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
- Huang, Yifan & Meng, Shengwang, 2020. "A Bayesian nonparametric model and its application in insurance loss prediction," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 84-94.
- Zhang, Junyi & Dassios, Angelos, 2024. "Posterior sampling from truncated Ferguson-Klass representation of normalised completely random measure mixtures," LSE Research Online Documents on Economics 122228, London School of Economics and Political Science, LSE Library.
- Tonellato, Stefano F., 2020. "Bayesian nonparametric clustering as a community detection problem," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
- Georgios Tsiotas, 2020. "On the use of power transformations in CAViaR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 296-312, March.
- Mingan Yang & David Dunson, 2010. "Bayesian Semiparametric Structural Equation Models with Latent Variables," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 675-693, December.
- Patricia Gilholm & Kerrie Mengersen & Helen Thompson, 2020. "Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-17, June.
- Alexandros Beskos & Omiros Papaspiliopoulos & Gareth O. Roberts & Paul Fearnhead, 2006.
"Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion),"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 333-382, June.
Cited by:
- Isambi Mbalawata & Simo Särkkä & Heikki Haario, 2013. "Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering," Computational Statistics, Springer, vol. 28(3), pages 1195-1223, June.
- Umberto Picchini & Andrea De Gaetano & Susanne Ditlevsen, 2010. "Stochastic Differential Mixed‐Effects Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 67-90, March.
- Nina Munkholt Jakobsen & Michael Sørensen, 2015. "Efficient Estimation for Diffusions Sampled at High Frequency Over a Fixed Time Interval," CREATES Research Papers 2015-33, Department of Economics and Business Economics, Aarhus University.
- Sourav Majumdar & Arnab Kumar Laha, 2024. "Diffusion on the circle and a stochastic correlation model," Papers 2412.06343, arXiv.org.
- Comte, F. & Genon-Catalot, V. & Rozenholc, Y., 2009. "Nonparametric adaptive estimation for integrated diffusions," Stochastic Processes and their Applications, Elsevier, vol. 119(3), pages 811-834, March.
- Kyoung-Kuk Kim & Sojung Kim, 2016. "Simulation of Tempered Stable Lévy Bridges and Its Applications," Operations Research, INFORMS, vol. 64(2), pages 495-509, April.
- Qihong Duan & Junrong Liu, 2015. "A first step to implement Gillespie’s algorithm with rejection sampling," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 85-95, March.
- Shoji, Isao, 2013. "Filtering for partially observed diffusion and its applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 4966-4976.
- Hermann Singer, 2014. "Importance sampling for Kolmogorov backward equations," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(4), pages 345-369, October.
- Kalogeropoulos, Konstantinos & Roberts, Gareth O. & Dellaportas, Petros, 2010.
"Inference for stochastic volatility models using time change transformations,"
LSE Research Online Documents on Economics
31421, London School of Economics and Political Science, LSE Library.
- Kalogeropoulos, Konstantinos & Roberts, Gareth O. & Dellaportas, Petros, 2007. "Inference for stochastic volatility model using time change transformations," MPRA Paper 5697, University Library of Munich, Germany.
- Konstantinos Kalogeropoulos & Gareth O. Roberts & Petros Dellaportas, 2007. "Inference for stochastic volatility models using time change transformations," Papers 0711.1594, arXiv.org.
- Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Particle filters for partially observed diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 755-777, September.
- Markussen, Bo, 2009. "Laplace approximation of transition densities posed as Brownian expectations," Stochastic Processes and their Applications, Elsevier, vol. 119(1), pages 208-231, January.
- Gareth W. Peters & Rodrigo S. Targino & Mario V. Wüthrich, 2017. "Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks," Risks, MDPI, vol. 5(4), pages 1-51, September.
- Kalogeropoulos, Konstantinos & Dellaportas, Petros & Roberts, Gareth O., 2007.
"Likelihood-based inference for correlated diffusions,"
MPRA Paper
5696, University Library of Munich, Germany.
- Konstantinos Kalogeropoulos & Petros Dellaportas & Gareth O. Roberts, 2007. "Likelihood-based inference for correlated diffusions," Papers 0711.1595, arXiv.org.
- Vinícius Diniz Mayrink & Flávio Bambirra Gonçalves, 2017. "A Bayesian hidden Markov mixture model to detect overexpressed chromosome regions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 387-412, February.
- Julie Lyng Forman & Michael Sørensen, 2008.
"The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes,"
Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465, September.
- Michael Sørensen & Julie Lyng Forman, 2007. "The Pearson diffusions: A class of statistically tractable diffusion processes," CREATES Research Papers 2007-28, Department of Economics and Business Economics, Aarhus University.
- Kalogeropoulos, Konstantinos, 2007. "Likelihood-based inference for a class of multivariate diffusions with unobserved paths," LSE Research Online Documents on Economics 31423, London School of Economics and Political Science, LSE Library.
- Picchini, Umberto & Anderson, Rachele, 2017. "Approximate maximum likelihood estimation using data-cloning ABC," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 166-183.
- Paul Fearnhead & Vasilieos Giagos & Chris Sherlock, 2014. "Inference for reaction networks using the linear noise approximation," Biometrics, The International Biometric Society, vol. 70(2), pages 457-466, June.
- Czellar, Veronika & Karolyi, G. Andrew & Ronchetti, Elvezio, 2005.
"Indirect Robust Estimation of the Short-term Interest Rate Process,"
Working Paper Series
2005-4, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
- Veronika Czellar & G. Andrew Karolyi & Elvezio Ronchetti, 2005. "Indirect Robust Estimation of the Short-term interest Rate Process," FAME Research Paper Series rp135, International Center for Financial Asset Management and Engineering.
- Veronika Czellar & G. Andrew Karolyi & Elvezio Ronchetti, 2007. "Indirect robust estimation of the short-term interest rate process," Post-Print hal-00463251, HAL.
- Czellar, Veronika & Karolyi, G. Andrew & Ronchetti, Elvezio, 2007. "Indirect robust estimation of the short-term interest rate process," Journal of Empirical Finance, Elsevier, vol. 14(4), pages 546-563, September.
- Sy-Miin Chow & Zhaohua Lu & Andrew Sherwood & Hongtu Zhu, 2016. "Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 102-134, March.
- Yuan Shen & Dan Cornford & Manfred Opper & Cedric Archambeau, 2012. "Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions," Computational Statistics, Springer, vol. 27(1), pages 149-176, March.
- Varughese, Melvin M., 2013. "Parameter estimation for multivariate diffusion systems," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 417-428.
- Zhao-Hua Lu & Sy-Miin Chow & Nilam Ram & Pamela M. Cole, 2019. "Zero-Inflated Regime-Switching Stochastic Differential Equation Models for Highly Unbalanced Multivariate, Multi-Subject Time-Series Data," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 611-645, June.
- Beskos, Alexandros & Kalogeropoulos, Konstantinos & Pazos, Erik, 2013.
"Advanced MCMC methods for sampling on diffusion pathspace,"
Stochastic Processes and their Applications, Elsevier, vol. 123(4), pages 1415-1453.
- Beskos, Alexandros & Kalogeropoulos, Konstantinos & Pazos, Erik, 2013. "Advanced MCMC methods for sampling on diffusion pathspace," LSE Research Online Documents on Economics 46433, London School of Economics and Political Science, LSE Library.
- Theodore Simos & Mike Tsionas, 2018. "Bayesian inference of the fractional Ornstein–Uhlenbeck process under a flow sampling scheme," Computational Statistics, Springer, vol. 33(4), pages 1687-1713, December.
- Christian P. Robert & Gareth Roberts, 2021. "Rao–Blackwellisation in the Markov Chain Monte Carlo Era," International Statistical Review, International Statistical Institute, vol. 89(2), pages 237-249, August.
- Masayuki Uchida, 2010. "Contrast-based information criterion for ergodic diffusion processes from discrete observations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 161-187, February.
- Wanmo Kang & Jong Mun Lee, 2019. "Unbiased Sensitivity Estimation of One-Dimensional Diffusion Processes," Mathematics of Operations Research, INFORMS, vol. 44(1), pages 334-353, February.
- Nafidi, A. & Gutiérrez, R. & Gutiérrez-Sánchez, R. & Ramos-Ábalos, E. & El Hachimi, S., 2016. "Modelling and predicting electricity consumption in Spain using the stochastic Gamma diffusion process with exogenous factors," Energy, Elsevier, vol. 113(C), pages 309-318.
- Golightly, A. & Wilkinson, D.J., 2008. "Bayesian inference for nonlinear multivariate diffusion models observed with error," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1674-1693, January.
- Lee, Yoon Dong & Song, Seongjoo & Lee, Eun-Kyung, 2014. "The delta expansion for the transition density of diffusion models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 694-705.
- Mogens Bladt & Samuel Finch & Michael Sørensen, 2016.
"Simulation of multivariate diffusion bridges,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 343-369, March.
- Mogens Bladt & Samuel Finch & Michael Sørensen, 2014. "Simulation of multivariate diffusion bridges," CREATES Research Papers 2014-16, Department of Economics and Business Economics, Aarhus University.
- Bin Zhu & Peter X.-K. Song & Jeremy M.G. Taylor, 2011. "Stochastic Functional Data Analysis: A Diffusion Model-Based Approach," Biometrics, The International Biometric Society, vol. 67(4), pages 1295-1304, December.
- Rosen, Ori & Thompson, Wesley K., 2009. "A Bayesian regression model for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3773-3786, September.
- Kevin W. Lu & Phillip J. Paine & Simon P. Preston & Andrew T. A. Wood, 2022. "Approximate maximum likelihood estimation for one‐dimensional diffusions observed on a fine grid," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1085-1114, September.
- Osnat Stramer & Jun Yan, 2007. "Asymptotics of an Efficient Monte Carlo Estimation for the Transition Density of Diffusion Processes," Methodology and Computing in Applied Probability, Springer, vol. 9(4), pages 483-496, December.
- Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
- Paul Fearnhead & Omiros Papaspiliopoulos & Gareth O. Roberts & Andrew Stuart, 2010. "Random‐weight particle filtering of continuous time processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 497-512, September.
- Frank G. Ball & Ian L. Dryden & Mousa Golalizadeh, 2008. "Brownian Motion and Ornstein–Uhlenbeck Processes in Planar Shape Space," Methodology and Computing in Applied Probability, Springer, vol. 10(1), pages 1-22, March.
- van der Meulen, Frank & Schauer, Moritz & van Zanten, Harry, 2014. "Reversible jump MCMC for nonparametric drift estimation for diffusion processes," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 615-632.
- Yvo Pokern & Andrew M. Stuart & Petter Wiberg, 2009. "Parameter estimation for partially observed hypoelliptic diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 49-73, January.
- Eva María Ramos-Ábalos & Ramón Gutiérrez-Sánchez & Ahmed Nafidi, 2020. "Powers of the Stochastic Gompertz and Lognormal Diffusion Processes, Statistical Inference and Simulation," Mathematics, MDPI, vol. 8(4), pages 1-13, April.
- Chang, Jinyuan & Chen, Songxi, 2011. "On the Approximate Maximum Likelihood Estimation for Diffusion Processes," MPRA Paper 46279, University Library of Munich, Germany.
- Peter J. Diggle & Raquel Menezes & Ting‐li Su, 2010. "Geostatistical inference under preferential sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 191-232, March.
- Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.
- Salima El Kolei & Fabien Navarro, 2022. "Contrast estimation for noisy observations of diffusion processes via closed-form density expansions," Statistical Inference for Stochastic Processes, Springer, vol. 25(2), pages 303-336, July.
- Isadora Antoniano-Villalobos & Stephen G. Walker, 2016. "A Nonparametric Model for Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 126-142, January.
- Murray Pollock & Paul Fearnhead & Adam M. Johansen & Gareth O. Roberts, 2020. "Quasi‐stationary Monte Carlo and the ScaLE algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1167-1221, December.
- Matti Vihola & Jouni Helske & Jordan Franks, 2020. "Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1339-1376, December.
- J. O. Ramsay & G. Hooker & D. Campbell & J. Cao, 2007. "Parameter estimation for differential equations: a generalized smoothing approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 741-796, November.
- S. C. Kou & Benjamin P. Olding & Martin Lysy & Jun S. Liu, 2012. "A Multiresolution Method for Parameter Estimation of Diffusion Processes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1558-1574, December.
- Giorgos Sermaidis & Omiros Papaspiliopoulos & Gareth O. Roberts & Alexandros Beskos & Paul Fearnhead, 2013. "Markov Chain Monte Carlo for Exact Inference for Diffusions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(2), pages 294-321, June.
- Karl Friston, 2008. "Hierarchical Models in the Brain," PLOS Computational Biology, Public Library of Science, vol. 4(11), pages 1-24, November.
- DiCesare, Joe & Mcleish, Don, 2008. "Simulation of jump diffusions and the pricing of options," Insurance: Mathematics and Economics, Elsevier, vol. 43(3), pages 316-326, December.
- Hermann Singer, 2011. "Continuous-discrete state-space modeling of panel data with nonlinear filter algorithms," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 375-413, December.
- Christian P. Robert, 2013. "Bayesian Computational Tools," Working Papers 2013-45, Center for Research in Economics and Statistics.
- Murray, Lawrence M., 2015. "Bayesian State-Space Modelling on High-Performance Hardware Using LibBi," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i10).
- Quentin Clairon & Adeline Samson, 2020. "Optimal control for estimation in partially observed elliptic and hypoelliptic linear stochastic differential equations," Statistical Inference for Stochastic Processes, Springer, vol. 23(1), pages 105-127, April.
- Gareth O. Roberts & Omiros Papaspiliopoulos & Petros Dellaportas, 2004.
"Bayesian inference for non‐Gaussian Ornstein–Uhlenbeck stochastic volatility processes,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 369-393, May.
Cited by:
- Almut E. D. Veraart & Luitgard A. M. Veraart, 2009.
"Stochastic volatility and stochastic leverage,"
CREATES Research Papers
2009-20, Department of Economics and Business Economics, Aarhus University.
- Almut Veraart & Luitgard Veraart, 2012. "Stochastic volatility and stochastic leverage," Annals of Finance, Springer, vol. 8(2), pages 205-233, May.
- Marco Minozzo & Silvia Centanni, 2012. "Monte Carlo likelihood inference for marked doubly stochastic Poisson processes with intensity driven by marked point processes," Working Papers 11/2012, University of Verona, Department of Economics.
- Ole E. Barndorff-Nielsen & Elisa Nicolato & Neil Shephard, 2001.
"Some recent developments in stochastic volatility modelling,"
Economics Papers
2001-W25, Economics Group, Nuffield College, University of Oxford.
- Ole Barndorff-Nielsen & Elisa Nicolato & Neil Shephard, 2002. "Some recent developments in stochastic volatility modelling," Quantitative Finance, Taylor & Francis Journals, vol. 2(1), pages 11-23.
- Griffin, Jim & Steel, Mark F.J., 2008.
"Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes,"
MPRA Paper
11071, University Library of Munich, Germany.
- Griffin, J.E. & Steel, M.F.J., 2010. "Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2594-2608, November.
- Carl Lindberg, 2008. "The estimation of the Barndorff‐Nielsen and Shephard model from daily data based on measures of trading intensity," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(4), pages 277-289, July.
- Szczepocki Piotr, 2020. "Application of iterated filtering to stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck process," Statistics in Transition New Series, Statistics Poland, vol. 21(2), pages 173-187, June.
- Lancelot F. James, 2005. "Analysis of a Class of Likelihood Based Continuous Time Stochastic Volatility Models including Ornstein-Uhlenbeck Models in Financial Economics," Papers math/0503055, arXiv.org, revised Aug 2005.
- Almut E. D. Veraart, 2008. "Impact of time–inhomogeneous jumps and leverage type effects on returns and realised variances," CREATES Research Papers 2008-57, Department of Economics and Business Economics, Aarhus University.
- Emanuele Taufer, 2008. "Characteristic function estimation of non-Gaussian Ornstein-Uhlenbeck processes," DISA Working Papers 0805, Department of Computer and Management Sciences, University of Trento, Italy, revised 07 Jul 2008.
- Gregor Kastner & Sylvia Fruhwirth-Schnatter, 2017.
"Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models,"
Papers
1706.05280, arXiv.org.
- Kastner, Gregor & Frühwirth-Schnatter, Sylvia, 2014. "Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 408-423.
- Roberto Leon-Gonzalez, 2015.
"Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility,"
GRIPS Discussion Papers
15-17, National Graduate Institute for Policy Studies.
- Roberto Leon-Gonzalez, 2014. "Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility," GRIPS Discussion Papers 14-12, National Graduate Institute for Policy Studies.
- Roberto Leon-Gonzalez, 2014. "Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility," Working Paper series 19_14, Rimini Centre for Economic Analysis.
- Roberto Leon-Gonzalez, 2018. "Efficient Bayesian Inference in Generalized Inverse Gamma Processes for Stochastic Volatility," GRIPS Discussion Papers 17-16, National Graduate Institute for Policy Studies.
- Roberto León-González, 2019. "Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 899-920, September.
- Chris M Strickland & Gael Martin & Catherine S Forbes, 2006.
"Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models,"
Monash Econometrics and Business Statistics Working Papers
22/06, Monash University, Department of Econometrics and Business Statistics.
- Strickland, Chris M. & Martin, Gael M. & Forbes, Catherine S., 2008. "Parameterisation and efficient MCMC estimation of non-Gaussian state space models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2911-2930, February.
- Yan Qu & Angelos Dassios & Hongbiao Zhao, 2023. "Shot-noise cojumps: Exact simulation and option pricing," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 74(3), pages 647-665, March.
- Gonzalez, Jhonny & Moriarty, John & Palczewski, Jan, 2017. "Bayesian calibration and number of jump components in electricity spot price models," Energy Economics, Elsevier, vol. 65(C), pages 375-388.
- Creal, Drew D., 2008. "Analysis of filtering and smoothing algorithms for Lévy-driven stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2863-2876, February.
- Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2016. "A Multiscale Stochastic Conditional Duration Model," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 1-28, December.
- James Martin & Ajay Jasra & Emma McCoy, 2013. "Inference for a class of partially observed point process models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 413-437, June.
- Thomas von Brasch & Arvid Raknerud, 2021. "A two-stage pooled panel data estimator of demand elasticities," Discussion Papers 951, Statistics Norway, Research Department.
- Friedrich Hubalek & Petra Posedel, 2008. "Asymptotic analysis for a simple explicit estimator in Barndorff-Nielsen and Shephard stochastic volatility models," Papers 0807.3479, arXiv.org.
- Christian Laudag'e & Florian Aichinger & Sascha Desmettre, 2023. "A Comparative Study of Factor Models for Different Periods of the Electricity Spot Price Market," Papers 2306.07731, arXiv.org, revised Apr 2024.
- Sylvia Frühwirth-Schnatter & Leopold Sögner, 2009. "Bayesian estimation of stochastic volatility models based on OU processes with marginal Gamma law," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 159-179, March.
- Yan-Feng Wu & Xiangyu Yang & Jian-Qiang Hu, 2024. "Method of Moments Estimation for Affine Stochastic Volatility Models," Papers 2408.09185, arXiv.org.
- Taufer, Emanuele & Leonenko, Nikolai & Bee, Marco, 2011.
"Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models,"
Computational Statistics & Data Analysis, Elsevier, vol. 55(8), pages 2525-2539, August.
- Emanuele Taufer & Nikolai Leonenko & Marco Bee, 2009. "Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models," DISA Working Papers 0907, Department of Computer and Management Sciences, University of Trento, Italy, revised 02 Dec 2009.
- Shibin Zhang & Xinsheng Zhang, 2008. "Exact Simulation of IG-OU Processes," Methodology and Computing in Applied Probability, Springer, vol. 10(3), pages 337-355, September.
- Zhongxian Men & Tony S. Wirjanto & Adam W. Kolkiewicz, 2021. "Multiscale Stochastic Volatility Model with Heavy Tails and Leverage Effects," JRFM, MDPI, vol. 14(5), pages 1-28, May.
- Yijie Peng & Michael C. Fu & Jian-Qiang Hu, 2016. "Gradient-based simulated maximum likelihood estimation for stochastic volatility models using characteristic functions," Quantitative Finance, Taylor & Francis Journals, vol. 16(9), pages 1393-1411, September.
- Qu, Yan & Dassios, Angelos & Zhao, Hongbiao, 2023. "Shot-noise cojumps: exact simulation and option pricing," LSE Research Online Documents on Economics 111537, London School of Economics and Political Science, LSE Library.
- Asger Lunde & Anne Floor Brix & Wei Wei, 2015. "A Generalized Schwartz Model for Energy Spot Prices - Estimation using a Particle MCMC Method," CREATES Research Papers 2015-46, Department of Economics and Business Economics, Aarhus University.
- Fasen, Vicky, 2013. "Statistical estimation of multivariate Ornstein–Uhlenbeck processes and applications to co-integration," Journal of Econometrics, Elsevier, vol. 172(2), pages 325-337.
- Todorov, Viktor, 2011. "Econometric analysis of jump-driven stochastic volatility models," Journal of Econometrics, Elsevier, vol. 160(1), pages 12-21, January.
- N. Chopin & P. E. Jacob & O. Papaspiliopoulos, 2013. "SMC-super-2: an efficient algorithm for sequential analysis of state space models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 397-426, June.
- Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
- James E. Griffin & Mark F.J. Steel, 2002.
"Inference With Non-Gaussian Ornstein-Uhlenbeck Processes for Stochastic Volatility,"
Econometrics
0201002, University Library of Munich, Germany, revised 04 Apr 2003.
- Griffin, J.E. & Steel, M.F.J., 2006. "Inference with non-Gaussian Ornstein-Uhlenbeck processes for stochastic volatility," Journal of Econometrics, Elsevier, vol. 134(2), pages 605-644, October.
- Giorgos Sermaidis & Omiros Papaspiliopoulos & Gareth O. Roberts & Alexandros Beskos & Paul Fearnhead, 2013. "Markov Chain Monte Carlo for Exact Inference for Diffusions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(2), pages 294-321, June.
- Shu, Yin & Feng, Qianmei & Liu, Hao, 2019. "Using degradation-with-jump measures to estimate life characteristics of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
- Almut E. D. Veraart & Luitgard A. M. Veraart, 2009.
"Stochastic volatility and stochastic leverage,"
CREATES Research Papers
2009-20, Department of Economics and Business Economics, Aarhus University.