Bayesian non‐parametric hidden Markov models with applications in genomics
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- 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.
- Peter J. Green & Sylvia Richardson, 2001. "Modelling Heterogeneity With and Without the Dirichlet Process," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(2), pages 355-375, June.
- Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
- Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
- Guha, Subharup & Li, Yi & Neuberg, Donna, 2008. "Bayesian Hidden Markov Modeling of Array CGH Data," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 485-497, June.
- Kim, Chang-Jin, 1994.
"Dynamic linear models with Markov-switching,"
Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
- Kim, C-J., 1991. "Dynamic Linear Models with Markov-Switching," Papers 91-8, York (Canada) - Department of Economics.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- 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.
- 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.).
- 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.
- 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.
- 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.
- repec:dau:papers:123456789/13438 is not listed on IDEAS
- 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).
- 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.
- 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).
- 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).
- 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.
- 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.
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.- Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020.
"Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model,"
International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
- Heinrich, Markus & Carstensen, Kai & Reif, Magnus & Wolters, Maik, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168206, Verein für Socialpolitik / German Economic Association.
- Kai Carstensen & Markus Heinrich & Magnus Reif & Maik H. Wolters, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," CESifo Working Paper Series 6457, CESifo.
- Kai Carstensen & Markus Heinrich & Magnus Reif & Maik H. Wolters, 2019. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model," Jena Economics Research Papers 2019-006, Friedrich-Schiller-University Jena.
- Perron, Pierre & Wada, Tatsuma, 2016.
"Measuring business cycles with structural breaks and outliers: Applications to international data,"
Research in Economics, Elsevier, vol. 70(2), pages 281-303.
- Tatsuma Wada & Pierre Perron, 2014. "Measuring Business Cycles with Structural Breaks and Outliers: Applications to International Data," Boston University - Department of Economics - Working Papers Series 2014-004, Boston University - Department of Economics.
- Pierre Perron & Tatsuma Wada, 2015. "Measuring Business Cycles with Structural Breaks and Outliers: Applications to International Data," Boston University - Department of Economics - Working Papers Series wp2015-016, Boston University - Department of Economics.
- Carol Alexander & Anca Dimitriu, 2003. "Equity Indexing: Conitegration and Stock Price Dispersion: A Regime Switiching Approach to market Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2003-02, Henley Business School, University of Reading.
- David Bolder & Shudan Liu, 2007. "Examining Simple Joint Macroeconomic and Term-Structure Models: A Practitioner's Perspective," Staff Working Papers 07-49, Bank of Canada.
- Alain Monfort & Olivier Féron, 2012.
"Joint econometric modeling of spot electricity prices, forwards and options,"
Review of Derivatives Research, Springer, vol. 15(3), pages 217-256, October.
- Alain Monfort & Olivier Féron, 2011. "Joint Econometric Modeling of Spot Electricity Prices, Forwards and Options," Working Papers 2011-12, Center for Research in Economics and Statistics.
- Gary Koop & Dimitris Korobilis, 2012.
"Forecasting Inflation Using Dynamic Model Averaging,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
- Gary Koop & Dimitris Korobilis, 2009. "Forecasting Inflation Using Dynamic Model Averaging," Working Paper series 34_09, Rimini Centre for Economic Analysis.
- Koop, Gary & Korobilis, Dimitris, 2011. "Forecasting Inflation Using Dynamic Model Averaging," SIRE Discussion Papers 2011-40, Scottish Institute for Research in Economics (SIRE).
- Koop, Gary & Korobilis, Dimitris, 2010. "Forecasting Inflation Using Dynamic Model Averaging," SIRE Discussion Papers 2010-113, Scottish Institute for Research in Economics (SIRE).
- Gary Koop & Dimitris Korobilis, 2011. "Forecasting Inflation Using Dynamic Model Averaging," Working Papers 1119, University of Strathclyde Business School, Department of Economics.
- Carol Alexander & Anca Dimitriu, 2005. "Indexing, cointegration and equity market regimes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 10(3), pages 213-231.
- Marie Bessec, 2019.
"Revisiting the transitional dynamics of business cycle phases with mixed-frequency data,"
Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 711-732, August.
- Marie Bessec, 2016. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Working Papers hal-01358595, HAL.
- Marie Bessec, 2019. "Revisiting the transitional dynamics of business-cycle phases with mixed-frequency data," Post-Print hal-02181552, HAL.
- Chauvet, Marcelle & Potter, Simon, 2001.
"Nonlinear Risk,"
Macroeconomic Dynamics, Cambridge University Press, vol. 5(4), pages 621-646, September.
- Marcelle Chauvet & Simon M. Potter, 1999. "Nonlinear risk," Staff Reports 61, Federal Reserve Bank of New York.
- Mehmet Pasaogullari & Simeon Tsonevy, 2011. "The term structure of inflation compensation in the nominal yield curve," Working Papers (Old Series) 1133, Federal Reserve Bank of Cleveland.
- Theobald, Thomas, 2013. "Markov Switching with Endogenous Number of Regimes and Leading Indicators in a Real-Time Business Cycle Forecast," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79911, Verein für Socialpolitik / German Economic Association.
- Carol Alexander & Anca Dimitriu, 2005. "Detecting Switching Strategies in Equity Hedge Funds," ICMA Centre Discussion Papers in Finance icma-dp2005-07, Henley Business School, University of Reading.
- Randolph & Xiao Qin & Tan Gee Kwang, 2004.
"Unit Root Tests with Markov-Switching,"
Econometric Society 2004 Australasian Meetings
145, Econometric Society.
- Xiao Qin & Gee Kwang Randolph Tan, 2005. "Unit Root Tests With Markov-Switching," Computing in Economics and Finance 2005 95, Society for Computational Economics.
- Dmitry Kulikov, 2012. "Testing for Rational Speculative Bubbles on the Estonian Stock Market," Research in Economics and Business: Central and Eastern Europe, Tallinn School of Economics and Business Administration, Tallinn University of Technology, vol. 4(1).
- Jin, Xin & Maheu, John M., 2016.
"Bayesian semiparametric modeling of realized covariance matrices,"
Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
- Jin, Xin & Maheu, John M, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," MPRA Paper 60102, University Library of Munich, Germany.
- Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
- Kahn, James A. & Rich, Robert W., 2007.
"Tracking the new economy: Using growth theory to detect changes in trend productivity,"
Journal of Monetary Economics, Elsevier, vol. 54(6), pages 1670-1701, September.
- James A. Kahn & Robert W. Rich, 2003. "Tracking the new economy: using growth theory to detect changes in trend productivity," Proceedings, Federal Reserve Bank of San Francisco, issue Nov.
- James A. Kahn & Robert W. Rich, 2003. "Tracking the new economy: using growth theory to detect changes in trend productivity," Staff Reports 159, Federal Reserve Bank of New York.
- Masaru Chiba, 2023. "Robust and efficient specification tests in Markov-switching autoregressive models," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 99-137, April.
- Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
- Mount, Timothy D. & Ning, Yumei & Cai, Xiaobin, 2006. "Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters," Energy Economics, Elsevier, vol. 28(1), pages 62-80, January.
- Konstantin A. Kholodilin, 2006.
"Using the Dynamic Bi-Factor Model with Markov Switching to Predict the Cyclical Turns in the Large European Economies,"
Discussion Papers of DIW Berlin
554, DIW Berlin, German Institute for Economic Research.
- Konstantin A. Kholodilin, 2007. "Using the Dynamic Bi-Factor Model with Markov Switching to Predict the Cyclical Turns in the Large European Economies," Money Macro and Finance (MMF) Research Group Conference 2006 13, Money Macro and Finance Research Group.
More about this item
Keywords
Block Gibbs sampler ; Copy number variation ; Local and global clustering ; Partial exchangeability ; Partition models ; Retrospective sampling ;All these keywords.
Statistics
Access and download statisticsCorrections
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:jorssb:v:73:y:2011:i:1:p:37-57. 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.