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AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
Migon, H.S., Lima, E.C.R. and Lopes, H.F. Efeitos dinamicos dos choques de oferta e demanda agregada sobre o nıvel de atividade economica do Brasil 1993 Revista Brasileira de Economia
Vol. 47, pp. 177-204 
article  
BibTeX:
@article{,
  author = {Helio S. Migon and Elcyon C. R. Lima and Hedibert F. Lopes},
  title = {Efeitos dinamicos dos choques de oferta e demanda agregada sobre o nıvel de atividade economica do Brasil},
  journal = {Revista Brasileira de Economia},
  year = {1993},
  volume = {47},
  pages = {177-204}
}
Lopes, H.F., Moreira, A.R. and Schmidt, A.M. Hyperparameter estimation in forecast models 1999 Computational Statistics & Data Analysis
Vol. 29(4), pp. 387-410 
article DOI  
BibTeX:
@article{Lopes1999,
  author = {Hedibert Freitas Lopes and Ajax R.Bello Moreira and Alexandra Mello Schmidt},
  title = {Hyperparameter estimation in forecast models},
  journal = {Computational Statistics & Data Analysis},
  publisher = {Elsevier BV},
  year = {1999},
  volume = {29},
  number = {4},
  pages = {387--410},
  doi = {https://doi.org/10.1016/s0167-9473(98)00078-4}
}
Huerta, G. and Lopes, H.F. Bayesian forecasting and inference in latent structure for the Brazilian Industrial Production Index 2000 Brazilian Review of Econometrics
Vol. 20(1), pp. 1 
article DOI  
BibTeX:
@article{Huerta2000,
  author = {Gabriel Huerta and Hedibert Freitas Lopes},
  title = {Bayesian forecasting and inference in latent structure for the Brazilian Industrial Production Index},
  journal = {Brazilian Review of Econometrics},
  publisher = {Fundacao Getulio Vargas},
  year = {2000},
  volume = {20},
  number = {1},
  pages = {1},
  doi = {https://doi.org/10.12660/bre.v20n12000.2772}
}
Lopes, H.F. and Migon, H.S. Comovements and contagion in emergent markets: stock indexes volatilities 2002 Case Studies in Bayesian Statistics
Vol. VI, pp. 285-300 
article  
BibTeX:
@article{Lopes2002,
  author = {Hedibert Freitas Lopes and Helio S Migon},
  title = {Comovements and contagion in emergent markets: stock indexes volatilities},
  journal = {Case Studies in Bayesian Statistics},
  year = {2002},
  volume = {VI},
  pages = {285--300}
}
Lopes, H.F., Müller, P. and Rosner, G.L. Bayesian Meta-analysis for Longitudinal Data Models Using Multivariate Mixture Priors 2003 Biometrics
Vol. 59(1), pp. 66-75 
article DOI  
BibTeX:
@article{Lopes2003,
  author = {Hedibert Freitas Lopes and Peter Müller and Gary L. Rosner},
  title = {Bayesian Meta-analysis for Longitudinal Data Models Using Multivariate Mixture Priors},
  journal = {Biometrics},
  publisher = {Wiley},
  year = {2003},
  volume = {59},
  number = {1},
  pages = {66--75},
  doi = {https://doi.org/10.1111/1541-0420.00008}
}
Lopes, H.F. Expected Posterior Priors in Factor Analysis 2003 Brazilian Journal of Probability and Statistics
Vol. 17(1), pp. 91-105 
article  
BibTeX:
@article{Lopes2003,
  author = {Hedibert Freitas Lopes},
  title = {Expected Posterior Priors in Factor Analysis},
  journal = {Brazilian Journal of Probability and Statistics},
  year = {2003},
  volume = {17},
  number = {1},
  pages = {91--105}
}
Behrens, C.N., Lopes, H.F. and Gamerman, D. Bayesian analysis of extreme events with threshold estimation 2004 Statistical Modelling
Vol. 4(3), pp. 227-244 
article DOI  
BibTeX:
@article{Behrens2004,
  author = {Cibele N Behrens and Hedibert F Lopes and Dani Gamerman},
  title = {Bayesian analysis of extreme events with threshold estimation},
  journal = {Statistical Modelling},
  publisher = {SAGE Publications},
  year = {2004},
  volume = {4},
  number = {3},
  pages = {227--244},
  doi = {https://doi.org/10.1191/1471082x04st075oa}
}
Lopes, H.F. and West, M. BAYESIAN MODEL ASSESSMENT IN FACTOR ANALYSIS 2004 Statistica Sinica
Vol. 14(1), pp. 41-67 
article  
BibTeX:
@article{Lopes2004,
  author = {Hedibert F. Lopes and Mike West},
  title = {BAYESIAN MODEL ASSESSMENT IN FACTOR ANALYSIS},
  journal = {Statistica Sinica},
  year = {2004},
  volume = {14},
  number = {1},
  pages = {41--67}
}
Mendes, B.V.d.M. and Lopes, H.F. Data driven estimates for mixtures 2004 Computational Statistics & Data Analysis
Vol. 47(3), pp. 583-598 
article DOI  
BibTeX:
@article{Mendes2004,
  author = {Mendes, Beatriz Vaz de Melo and Hedibert Freitas Lopes},
  title = {Data driven estimates for mixtures},
  journal = {Computational Statistics & Data Analysis},
  publisher = {Elsevier BV},
  year = {2004},
  volume = {47},
  number = {3},
  pages = {583--598},
  doi = {https://doi.org/10.1016/j.csda.2003.12.006}
}
Nobre, A.A., Schmidt, A.M. and Lopes, H.F. Spatio-temporal models for mapping the incidence of malaria in Pará 2005 Environmetrics
Vol. 16(3), pp. 291-304 
article DOI  
BibTeX:
@article{Nobre2005,
  author = {Aline A. Nobre and Alexandra M. Schmidt and Hedibert F. Lopes},
  title = {Spatio-temporal models for mapping the incidence of malaria in Pará},
  journal = {Environmetrics},
  publisher = {Wiley},
  year = {2005},
  volume = {16},
  number = {3},
  pages = {291--304},
  doi = {https://doi.org/10.1002/env.704}
}
Lopes, H.F. and Salazar, E. Bayesian Model Uncertainty In Smooth Transition Autoregressions 2006 Journal of Time Series Analysis
Vol. 27(1), pp. 99-117 
article DOI  
BibTeX:
@article{Lopes2006,
  author = {Hedibert F. Lopes and Esther Salazar},
  title = {Bayesian Model Uncertainty In Smooth Transition Autoregressions},
  journal = {Journal of Time Series Analysis},
  publisher = {Wiley},
  year = {2006},
  volume = {27},
  number = {1},
  pages = {99--117},
  doi = {https://doi.org/10.1111/j.1467-9892.2005.00455.x}
}
Silva, R.d.S., Lopes, H.F. and Migon, H.S. The Extended Generalized Inverse Gaussian Distribution for Log-linear and Stochastic Volatility Models 2006 Brazilian Journal of Probability and Statistics
Vol. 20(1), pp. 67-91 
article  
BibTeX:
@article{Silva2006,
  author = {Silva, Ralph dos Santos and Hedibert F. Lopes and Helio S. Migon},
  title = {The Extended Generalized Inverse Gaussian Distribution for Log-linear and Stochastic Volatility Models},
  journal = {Brazilian Journal of Probability and Statistics},
  year = {2006},
  volume = {20},
  number = {1},
  pages = {67--91}
}
Ausin, M.C. and Lopes, H.F. BAYESIAN ESTIMATION OF RUIN PROBABILITIES WITH A HETEROGENEOUS AND HEAVY-TAILED INSURANCE CLAIM-SIZE DISTRIBUTION 2007 Australian & New Zealand Journal of Statistics
Vol. 49(4), pp. 415-434 
article DOI  
BibTeX:
@article{Ausin2007,
  author = {M. Concepcion Ausin and Hedibert F. Lopes},
  title = {BAYESIAN ESTIMATION OF RUIN PROBABILITIES WITH A HETEROGENEOUS AND HEAVY-TAILED INSURANCE CLAIM-SIZE DISTRIBUTION},
  journal = {Australian & New Zealand Journal of Statistics},
  publisher = {Wiley},
  year = {2007},
  volume = {49},
  number = {4},
  pages = {415--434},
  doi = {https://doi.org/10.1111/j.1467-842x.2007.00492.x}
}
Carvalho, C.M. and Lopes, H.F. Simulation-based sequential analysis of Markov switching stochastic volatility models 2007 Computational Statistics & Data Analysis
Vol. 51(9), pp. 4526-4542 
article DOI  
BibTeX:
@article{Carvalho2007,
  author = {Carlos M. Carvalho and Hedibert F. Lopes},
  title = {Simulation-based sequential analysis of Markov switching stochastic volatility models},
  journal = {Computational Statistics & Data Analysis},
  publisher = {Elsevier BV},
  year = {2007},
  volume = {51},
  number = {9},
  pages = {4526--4542},
  doi = {https://doi.org/10.1016/j.csda.2006.07.019}
}
Lopes, H.F. and Carvalho, C.M. Factor stochastic volatility with time varying loadings and Markov switching regimes 2007 Journal of Statistical Planning and Inference
Vol. 137(10), pp. 3082-3091 
article DOI  
BibTeX:
@article{Lopes2007,
  author = {Hedibert Freitas Lopes and Carlos M. Carvalho},
  title = {Factor stochastic volatility with time varying loadings and Markov switching regimes},
  journal = {Journal of Statistical Planning and Inference},
  publisher = {Elsevier BV},
  year = {2007},
  volume = {137},
  number = {10},
  pages = {3082--3091},
  doi = {https://doi.org/10.1016/j.jspi.2006.06.047}
}
Lopes, H.F., Salazar, E. and Gamerman, D. Spatial dynamic factor analysis 2008 Bayesian Analysis
Vol. 3(4), pp. 759-792 
article DOI  
BibTeX:
@article{Lopes2008,
  author = {Hedibert Freitas Lopes and Esther Salazar and Dani Gamerman},
  title = {Spatial dynamic factor analysis},
  journal = {Bayesian Analysis},
  publisher = {Institute of Mathematical Statistics},
  year = {2008},
  volume = {3},
  number = {4},
  pages = {759--792},
  doi = {https://doi.org/10.1214/08-ba329}
}
Silva, R.d.S. and Lopes, H.F. Copula, marginal distributions and model selection: a Bayesian note 2008 Statistics and Computing
Vol. 18(3), pp. 313-320 
article DOI  
BibTeX:
@article{Silva2008,
  author = {Silva, Ralph dos Santos and Hedibert Freitas Lopes},
  title = {Copula, marginal distributions and model selection: a Bayesian note},
  journal = {Statistics and Computing},
  publisher = {Springer Science and Business Media LLC},
  year = {2008},
  volume = {18},
  number = {3},
  pages = {313--320},
  doi = {https://doi.org/10.1007/s11222-008-9058-y}
}
Vibranovski, M.D., Lopes, H.F., Karr, T.L. and Long, M. Stage-Specific Expression Profiling of Drosophila Spermatogenesis Suggests that Meiotic Sex Chromosome Inactivation Drives Genomic Relocation of Testis-Expressed Genes 2009 PLoS Genetics
Vol. 5(11), pp. e1000731 
article DOI  
BibTeX:
@article{Vibranovski2009,
  author = {Maria D. Vibranovski and Hedibert F. Lopes and Timothy L. Karr and Manyuan Long},
  title = {Stage-Specific Expression Profiling of Drosophila Spermatogenesis Suggests that Meiotic Sex Chromosome Inactivation Drives Genomic Relocation of Testis-Expressed Genes},
  journal = {PLoS Genetics},
  publisher = {Public Library of Science (PLoS)},
  year = {2009},
  volume = {5},
  number = {11},
  pages = {e1000731},
  doi = {https://doi.org/10.1371/journal.pgen.1000731}
}
Abanto-Valle, C.A., Migon, H.S. and Lopes, H.F. Bayesian modeling of financial returns: A relationship between volatility and trading volume 2010 Applied Stochastic Models in Business and Industry
Vol. 26(2), pp. 172-193 
article DOI  
BibTeX:
@article{AbantoValle2010,
  author = {Carlos A. Abanto-Valle and Helio S. Migon and Hedibert F. Lopes},
  title = {Bayesian modeling of financial returns: A relationship between volatility and trading volume},
  journal = {Applied Stochastic Models in Business and Industry},
  publisher = {Wiley},
  year = {2010},
  volume = {26},
  number = {2},
  pages = {172--193},
  doi = {https://doi.org/10.1002/asmb.789}
}
Ausin, M.C. and Lopes, H.F. Time-varying joint distribution through copulas 2010 Computational Statistics & Data Analysis
Vol. 54(11), pp. 2383-2399 
article DOI  
BibTeX:
@article{Ausin2010,
  author = {M. Concepcion Ausin and Hedibert F. Lopes},
  title = {Time-varying joint distribution through copulas},
  journal = {Computational Statistics & Data Analysis},
  publisher = {Elsevier BV},
  year = {2010},
  volume = {54},
  number = {11},
  pages = {2383--2399},
  doi = {https://doi.org/10.1016/j.csda.2009.03.008}
}
Carvalho, C.M., Johannes, M.S., Lopes, H.F. and Polson, N.G. Particle Learning and Smoothing 2010 Statistical Science 2010, Vol. 25, No. 1, 88-106  article DOI  
Abstract: Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.
BibTeX:
@article{Carvalho2010,
  author = {Carlos M. Carvalho and Michael S. Johannes and Hedibert F. Lopes and Nicholas G. Polson},
  title = {Particle Learning and Smoothing},
  journal = {Statistical Science 2010, Vol. 25, No. 1, 88-106},
  year = {2010},
  doi = {https://doi.org/10.1214/10-STS325}
}
Carvalho, C.M., Lopes, H.F., Polson, N.G. and Taddy, M.A. Particle learning for general mixtures 2010 Bayesian Analysis
Vol. 5(4), pp. 709-740 
article DOI  
BibTeX:
@article{Carvalho2010a,
  author = {Carlos M. Carvalho and Hedibert F. Lopes and Nicholas G. Polson and Matt A. Taddy},
  title = {Particle learning for general mixtures},
  journal = {Bayesian Analysis},
  publisher = {Institute of Mathematical Statistics},
  year = {2010},
  volume = {5},
  number = {4},
  pages = {709--740},
  doi = {https://doi.org/10.1214/10-ba525}
}
Hore, S., Johannes, M., Lopes, H.F., Mcculloch, R. and Polson, N.G. Bayesian computation in finance 2010 Frontiers of Statistical Decision Making and and Bayesian Analysis - In Honor of James O. Berger, pp. 383-96  inbook  
BibTeX:
@inbook{Hore2010,
  author = {Satadru Hore and Michael Johannes and Hedibert F. Lopes and Robert Mcculloch and Nicholas G. Polson},
  title = {Bayesian computation in finance},
  booktitle = {Frontiers of Statistical Decision Making and and Bayesian Analysis - In Honor of James O. Berger},
  year = {2010},
  pages = {383--96}
}
Lopes, H.F. and Tsay, R.S. Particle filters and Bayesian inference in financial econometrics 2010 Journal of Forecasting
Vol. 30(1), pp. 168-209 
article DOI  
BibTeX:
@article{Lopes2010,
  author = {Hedibert F. Lopes and Ruey S. Tsay},
  title = {Particle filters and Bayesian inference in financial econometrics},
  journal = {Journal of Forecasting},
  publisher = {Wiley},
  year = {2010},
  volume = {30},
  number = {1},
  pages = {168--209},
  doi = {https://doi.org/10.1002/for.1195}
}
Lopes, H.F. and Polson, N.G. Extracting SP500 and NASDAQ volatility: The credit crisis of 2010 Handbook of Applied Bayesian Analysis, pp. 319-361  inbook  
BibTeX:
@inbook{Lopes2010,
  author = {Hedibert F. Lopes and Nicholas G. Polson},
  title = {Extracting SP500 and NASDAQ volatility: The credit crisis of},
  booktitle = {Handbook of Applied Bayesian Analysis},
  year = {2010},
  pages = {319--361}
}
Nascimento, F.F.d., Gamerman, D. and Lopes, H.F. Regression models for exceedance data via the full likelihood 2010 Environmental and Ecological Statistics
Vol. 18(3), pp. 495-512 
article DOI  
BibTeX:
@article{Nascimento2010,
  author = {Nascimento, Fernando Ferraz do and Dani Gamerman and Hedibert Freitas Lopes},
  title = {Regression models for exceedance data via the full likelihood},
  journal = {Environmental and Ecological Statistics},
  publisher = {Springer Science and Business Media LLC},
  year = {2010},
  volume = {18},
  number = {3},
  pages = {495--512},
  doi = {https://doi.org/10.1007/s10651-010-0148-6}
}
Vibranovski, M.D., Chalopin, D.S., Lopes, H.F., Long, M. and Karr, T.L. Direct Evidence for Postmeiotic Transcription During Drosophila melanogaster Spermatogenesis 2010 Genetics
Vol. 186(1), pp. 431-433 
article DOI  
BibTeX:
@article{Vibranovski2010,
  author = {Maria D. Vibranovski and Domitille S. Chalopin and Hedibert F. Lopes and Manyuan Long and Timothy L. Karr},
  title = {Direct Evidence for Postmeiotic Transcription During Drosophila melanogaster Spermatogenesis},
  journal = {Genetics},
  publisher = {Oxford University Press (OUP)},
  year = {2010},
  volume = {186},
  number = {1},
  pages = {431--433},
  doi = {https://doi.org/10.1534/genetics.110.118919}
}
Carvalho, C.M., Lopes, H.F. and Aguilar, O. Dynamic stock selection strategies: A structured factor model approach (with discussion) 2011 Bayesian Statistics 9, pp. 69-90  inbook  
BibTeX:
@inbook{Carvalho2011,
  author = {Carlos M. Carvalho and Hedibert F. Lopes and Omar Aguilar},
  title = {Dynamic stock selection strategies: A structured factor model approach (with discussion)},
  booktitle = {Bayesian Statistics 9},
  year = {2011},
  pages = {69--90}
}
Lopes, H.F., Carvalho, C.M., Polson, N.G. and Johannes, M. Particle learning for sequential Bayesian computation (with discussion) 2011 Bayesian Statistics 9, pp. 317-360  inbook  
BibTeX:
@inbook{Lopes2011,
  author = {Hedibert F. Lopes and Carlos M. Carvalho and Nicholas G. Polson and Michael Johannes},
  title = {Particle learning for sequential Bayesian computation (with discussion)},
  booktitle = {Bayesian Statistics 9},
  year = {2011},
  pages = {317--360}
}
Lopes, H.F., Gamerman, D. and Salazar, E. Generalized spatial dynamic factor models 2011 Computational Statistics & Data Analysis
Vol. 55(3), pp. 1319-1330 
article DOI  
BibTeX:
@article{Lopes2011a,
  author = {Hedibert Freitas Lopes and Dani Gamerman and Esther Salazar},
  title = {Generalized spatial dynamic factor models},
  journal = {Computational Statistics & Data Analysis},
  publisher = {Elsevier BV},
  year = {2011},
  volume = {55},
  number = {3},
  pages = {1319--1330},
  doi = {https://doi.org/10.1016/j.csda.2010.09.020}
}
Lopes, H.F. and Tobias, J.L. Confronting Prior Convictions: On Issues of Prior Sensitivity and Likelihood Robustness in Bayesian Analysis 2011 Annual Review of Economics
Vol. 3(1), pp. 107-131 
article DOI  
BibTeX:
@article{Lopes2011b,
  author = {Hedibert F. Lopes and Justin L. Tobias},
  title = {Confronting Prior Convictions: On Issues of Prior Sensitivity and Likelihood Robustness in Bayesian Analysis},
  journal = {Annual Review of Economics},
  publisher = {Annual Reviews},
  year = {2011},
  volume = {3},
  number = {1},
  pages = {107--131},
  doi = {https://doi.org/10.1146/annurev-economics-111809-125134}
}
Nascimento, F.F.d., Gamerman, D. and Lopes, H.F. A semiparametric Bayesian approach to extreme value estimation 2011 Statistics and Computing
Vol. 22(2), pp. 661-675 
article DOI  
BibTeX:
@article{Nascimento2011,
  author = {Nascimento, Fernando Ferraz do and Dani Gamerman and Hedibert Freitas Lopes},
  title = {A semiparametric Bayesian approach to extreme value estimation},
  journal = {Statistics and Computing},
  publisher = {Springer Science and Business Media LLC},
  year = {2011},
  volume = {22},
  number = {2},
  pages = {661--675},
  doi = {https://doi.org/10.1007/s11222-011-9270-z}
}
Prado, R. and Lopes, H.F. Sequential parameter learning and filtering in structured autoregressive state-space models 2011 Statistics and Computing
Vol. 23(1), pp. 43-57 
article DOI  
BibTeX:
@article{Prado2011,
  author = {Raquel Prado and Hedibert F. Lopes},
  title = {Sequential parameter learning and filtering in structured autoregressive state-space models},
  journal = {Statistics and Computing},
  publisher = {Springer Science and Business Media LLC},
  year = {2011},
  volume = {23},
  number = {1},
  pages = {43--57},
  doi = {https://doi.org/10.1007/s11222-011-9289-1}
}
Zambaldi, F., Aranha, F., Lopes, H. and Politi, R. Credit granting to small firms: A Brazilian case 2011 Journal of Business Research
Vol. 64(3), pp. 309-315 
article DOI  
BibTeX:
@article{Zambaldi2011,
  author = {Felipe Zambaldi and Francisco Aranha and Hedibert Lopes and Ricardo Politi},
  title = {Credit granting to small firms: A Brazilian case},
  journal = {Journal of Business Research},
  publisher = {Elsevier BV},
  year = {2011},
  volume = {64},
  number = {3},
  pages = {309--315},
  doi = {https://doi.org/10.1016/j.jbusres.2009.11.018}
}
Dukic, "., Vanja, H., Lopes, N. and Polson Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model 2012 Journal of the American Statistical Association
Vol. 107(500), pp. 1410-426 
article  
BibTeX:
@article{Dukic2012,
  author = {Dukic, " and Vanja, Hedibert and Lopes, Nicholas and Polson},
  title = {Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model},
  journal = {Journal of the American Statistical Association},
  year = {2012},
  volume = {107},
  number = {500},
  pages = {1410--426}
}
Lopes, H.F., Schmidt, A.M., Salazar, E., Gómez, M. and Achkar, M. Measuring the vulnerability of the Uruguayan population to vector-borne diseases via spatially hierarchical factor models 2012 The Annals of Applied Statistics
Vol. 6(1), pp. 284-303 
article DOI  
BibTeX:
@article{Lopes_2012,
  author = {Hedibert F. Lopes and Alexandra M. Schmidt and Esther Salazar and Mariana Gómez and Marcel Achkar},
  title = {Measuring the vulnerability of the Uruguayan population to vector-borne diseases via spatially hierarchical factor models},
  journal = {The Annals of Applied Statistics},
  publisher = {Institute of Mathematical Statistics},
  year = {2012},
  volume = {6},
  number = {1},
  pages = {284--303},
  doi = {https://doi.org/10.1214/11-aoas497}
}
Lopes, H.F., Polson, N.G. and Carvalho, C.M. Bayesian statistics with a smile: A resampling–sampling perspective 2012 Brazilian Journal of Probability and Statistics
Vol. 26(4), pp. 358-371 
article DOI  
BibTeX:
@article{Lopes2012,
  author = {Hedibert F. Lopes and Nicholas G. Polson and Carlos M. Carvalho},
  title = {Bayesian statistics with a smile: A resampling–sampling perspective},
  journal = {Brazilian Journal of Probability and Statistics},
  publisher = {Institute of Mathematical Statistics},
  year = {2012},
  volume = {26},
  number = {4},
  pages = {358--371},
  doi = {https://doi.org/10.1214/11-bjps144}
}
Vibranovski, M.D., Zhang, Y.E., Kemkemer, C., VanKuren, N.W., Lopes, H.F., Karr, T.L. and Long, M. Segmental dataset and whole body expression data do not support the hypothesis that non-random movement is an intrinsic property of Drosophila retrogenes 2012 BMC Evolutionary Biology
Vol. 12(1), pp. 169 
article DOI  
BibTeX:
@article{Vibranovski2012,
  author = {Maria D Vibranovski and Yong E Zhang and Claus Kemkemer and Nicholas W VanKuren and Hedibert F Lopes and Timothy L Karr and Manyuan Long},
  title = {Segmental dataset and whole body expression data do not support the hypothesis that non-random movement is an intrinsic property of Drosophila retrogenes},
  journal = {BMC Evolutionary Biology},
  publisher = {Springer Science and Business Media LLC},
  year = {2012},
  volume = {12},
  number = {1},
  pages = {169},
  doi = {https://doi.org/10.1186/1471-2148-12-169}
}
Vibranovski, M.D., Zhang, Y.E., Kemkemer, C., Lopes, H.F., Karr, T.L. and Long, M. Re-analysis of the larval testis data on meiotic sex chromosome inactivation revealed evidence for tissue-specific gene expression related to the drosophila X chromosome 2012 BMC Biology
Vol. 10(1) 
article DOI  
BibTeX:
@article{Vibranovski2012a,
  author = {Maria D Vibranovski and Yong E Zhang and Claus Kemkemer and Hedibert F Lopes and Timothy L Karr and Manyuan Long},
  title = {Re-analysis of the larval testis data on meiotic sex chromosome inactivation revealed evidence for tissue-specific gene expression related to the drosophila X chromosome},
  journal = {BMC Biology},
  publisher = {Springer Science and Business Media LLC},
  year = {2012},
  volume = {10},
  number = {1},
  doi = {https://doi.org/10.1186/1741-7007-10-49}
}
Heckman, J.J., Lopes, H.F. and Piatek, R. Treatment Effects: A Bayesian Perspective 2013 Econometric Reviews
Vol. 33(1-4), pp. 36-67 
article DOI  
BibTeX:
@article{Heckman2013,
  author = {James J. Heckman and Hedibert F. Lopes and Rémi Piatek},
  title = {Treatment Effects: A Bayesian Perspective},
  journal = {Econometric Reviews},
  publisher = {Informa UK Limited},
  year = {2013},
  volume = {33},
  number = {1-4},
  pages = {36--67},
  doi = {https://doi.org/10.1080/07474938.2013.807103}
}
Kastner, G., Sylvia Fruhwirth, S. and Lopes, H.F. Analysis of exchange rates via multivariate Bayesian factor stochastic volatility models 2013 The Contribution of Young Researchers to Bayesian Statistics, pp. 181-186  inbook  
BibTeX:
@inbook{Kastner2013,
  author = {Gregor Kastner and Sylvia Fruhwirth,-Schnatter and Hedibert F. Lopes},
  title = {Analysis of exchange rates via multivariate Bayesian factor stochastic volatility models},
  booktitle = {The Contribution of Young Researchers to Bayesian Statistics},
  year = {2013},
  pages = {181--186}
}
Lopes, H.F. and Polson, N.G. Bayesian Instrumental Variables: Priors and Likelihoods 2013 Econometric Reviews
Vol. 33(1-4), pp. 100-121 
article DOI  
BibTeX:
@article{Lopes2013,
  author = {Hedibert F. Lopes and Nicholas G. Polson},
  title = {Bayesian Instrumental Variables: Priors and Likelihoods},
  journal = {Econometric Reviews},
  publisher = {Informa UK Limited},
  year = {2013},
  volume = {33},
  number = {1-4},
  pages = {100--121},
  doi = {https://doi.org/10.1080/07474938.2013.807146}
}
Lopes, H.F. and Carvalho, C.M. Online Bayesian learning in dynamic models: An illustrative introduction to particle methods 2013 Bayesian Theory and Applications, pp. 203-228  inbook  
BibTeX:
@inbook{Lopes2013a,
  author = {Hedibert F. Lopes and Carlos M. Carvalho},
  title = {Online Bayesian learning in dynamic models: An illustrative introduction to particle methods},
  booktitle = {Bayesian Theory and Applications},
  year = {2013},
  pages = {203--228}
}
Rios, M. and Lopes, H.F. The extended Liu and West filter: PL in MSSV models 2013 State-Space Models: Applications in Economics and Finance, pp. 23-61  inbook  
BibTeX:
@inbook{Rios2013,
  author = {Maria Rios and Hedibert F. Lopes},
  title = {The extended Liu and West filter: PL in MSSV models},
  booktitle = {State-Space Models: Applications in Economics and Finance},
  year = {2013},
  pages = {23--61}
}
Lopes, H.F. Lopes (2014) Modern Bayesian Factor Analysis 2014 Bayesian Inference in the Social Sciences, pp. 115-153  inbook  
BibTeX:
@inbook{Lopes2014,
  author = {Hedibert F. Lopes},
  title = {Lopes (2014) Modern Bayesian Factor Analysis},
  booktitle = {Bayesian Inference in the Social Sciences},
  publisher = {Wiley},
  year = {2014},
  pages = {115--153}
}
Lopes, H.F. and Polson, N.G. Particle Learning for Fat-Tailed Distributions 2015 Econometric Reviews
Vol. 35(8-10), pp. 1666-1691 
article DOI  
BibTeX:
@article{Lopes2015,
  author = {Hedibert F. Lopes and Nicholas G. Polson},
  title = {Particle Learning for Fat-Tailed Distributions},
  journal = {Econometric Reviews},
  publisher = {Informa UK Limited},
  year = {2015},
  volume = {35},
  number = {8-10},
  pages = {1666--1691},
  doi = {https://doi.org/10.1080/07474938.2015.1092809}
}
Lopes, H.F. and Dias, R. Bayesian mixture of parametric and nonparametric density estimation: A Misspecification Problem 2015 Brazilian Review of Econometrics
Vol. 31(1), pp. 19 
article DOI  
BibTeX:
@article{Lopes2015a,
  author = {Hedibert F. Lopes and Ronaldo Dias},
  title = {Bayesian mixture of parametric and nonparametric density estimation: A Misspecification Problem},
  journal = {Brazilian Review of Econometrics},
  publisher = {Fundacao Getulio Vargas},
  year = {2015},
  volume = {31},
  number = {1},
  pages = {19},
  doi = {https://doi.org/10.12660/bre.v31n12011.4134}
}
Nascimento, F.F.d., Gamerman, D. and Lopes, H.F. Time-varying extreme pattern with dynamic models 2015 TEST
Vol. 25(1), pp. 131-149 
article DOI  
BibTeX:
@article{Nascimento2015,
  author = {Nascimento, Fernando Ferraz do and Dani Gamerman and Hedibert Freitas Lopes},
  title = {Time-varying extreme pattern with dynamic models},
  journal = {TEST},
  publisher = {Springer Science and Business Media LLC},
  year = {2015},
  volume = {25},
  number = {1},
  pages = {131--149},
  doi = {https://doi.org/10.1007/s11749-015-0444-4}
}
Hahn, P.R., He, J. and Lopes, H. Bayesian Factor Model Shrinkage for Linear IV Regression With Many Instruments 2017 Journal of Business & Economic Statistics
Vol. 36(2), pp. 278-287 
article DOI  
BibTeX:
@article{Hahn2017,
  author = {P. Richard Hahn and Jingyu He and Hedibert Lopes},
  title = {Bayesian Factor Model Shrinkage for Linear IV Regression With Many Instruments},
  journal = {Journal of Business & Economic Statistics},
  publisher = {Informa UK Limited},
  year = {2017},
  volume = {36},
  number = {2},
  pages = {278--287},
  doi = {https://doi.org/10.1080/07350015.2016.1172968}
}
Kastner, G., Frühwirth-Schnatter, S. and Lopes, H.F. Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models 2017 Journal of Computational and Graphical Statistics
Vol. 26(4), pp. 905-917 
article DOI  
BibTeX:
@article{Kastner_2017,
  author = {Gregor Kastner and Sylvia Frühwirth-Schnatter and Hedibert Freitas Lopes},
  title = {Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models},
  journal = {Journal of Computational and Graphical Statistics},
  year = {2017},
  volume = {26},
  number = {4},
  pages = {905--917},
  doi = {https://doi.org/10.1080/10618600.2017.1322091}
}
Shirota, S., Omori, Y., Lopes, H.F. and Piao, H. Cholesky realized stochastic volatility model 2017 Econometrics and Statistics
Vol. 3, pp. 34-59 
article DOI  
BibTeX:
@article{Shirota2017,
  author = {Shinichiro Shirota and Yasuhiro Omori and Hedibert. F. Lopes and Haixiang Piao},
  title = {Cholesky realized stochastic volatility model},
  journal = {Econometrics and Statistics},
  publisher = {Elsevier BV},
  year = {2017},
  volume = {3},
  pages = {34--59},
  doi = {https://doi.org/10.1016/j.ecosta.2016.08.003}
}
Warty, S.P., Lopes, H.F. and Polson, N.G. Sequential Bayesian learning for stochastic volatility with variance-gamma jumps in returns 2017 Applied Stochastic Models in Business and Industry
Vol. 34(4), pp. 460-479 
article DOI  
BibTeX:
@article{Warty2017,
  author = {Samir P. Warty and Hedibert F. Lopes and Nicholas G. Polson},
  title = {Sequential Bayesian learning for stochastic volatility with variance-gamma jumps in returns},
  journal = {Applied Stochastic Models in Business and Industry},
  publisher = {Wiley},
  year = {2017},
  volume = {34},
  number = {4},
  pages = {460--479},
  doi = {https://doi.org/10.1002/asmb.2258}
}
Carvalho, C.M., Lopes, H.F. and McCulloch, R.E. On the Long-Run Volatility of Stocks 2018 Journal of the American Statistical Association
Vol. 113(523), pp. 1050-1069 
article DOI  
BibTeX:
@article{Carvalho2018,
  author = {Carlos M. Carvalho and Hedibert F. Lopes and Robert E. McCulloch},
  title = {On the Long-Run Volatility of Stocks},
  journal = {Journal of the American Statistical Association},
  publisher = {Informa UK Limited},
  year = {2018},
  volume = {113},
  number = {523},
  pages = {1050--1069},
  doi = {https://doi.org/10.1080/01621459.2017.1407769}
}
Fruehwirth-Schnatter, S. and Lopes, H.F. Sparse Bayesian Factor Analysis when the Number of Factors is Unknown 2018   article  
Abstract: Despite the popularity of sparse factor models, little attention has been given to formally address identifiability of these models beyond standard rotation-based identification such as the positive lower triangular constraint. To fill this gap, we provide a counting rule on the number of nonzero factor loadings that is sufficient for achieving uniqueness of the variance decomposition in the factor representation. Furthermore, we introduce the generalised lower triangular representation to resolve rotational invariance and show that within this model class the unknown number of common factors can be recovered in an overfitting sparse factor model. By combining point-mass mixture priors with a highly efficient and customised MCMC scheme, we obtain posterior summaries regarding the number of common factors as well as the factor loadings via postprocessing. Our methodology is illustrated for monthly exchange rates of 22 currencies with respect to the euro over a period of eight years and for monthly log returns of 73 firms from the NYSE100 over a period of 20 years.
BibTeX:
@article{FruehwirthSchnatter2018,
  author = {Sylvia Fruehwirth-Schnatter and Hedibert Freitas Lopes},
  title = {Sparse Bayesian Factor Analysis when the Number of Factors is Unknown},
  year = {2018}
}
Hahn, P.R., He, J. and Lopes, H.F. Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors 2018 Journal of Computational and Graphical Statistics
Vol. 28(1), pp. 142-154 
article DOI  
BibTeX:
@article{Hahn2018a,
  author = {P. Richard Hahn and Jingyu He and Hedibert F. Lopes},
  title = {Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors},
  journal = {Journal of Computational and Graphical Statistics},
  publisher = {Informa UK Limited},
  year = {2018},
  volume = {28},
  number = {1},
  pages = {142--154},
  doi = {https://doi.org/10.1080/10618600.2018.1482762}
}
Santos, P.H.F.d. and Lopes, H.F. Tree-Based Bayesian Treatment Effect Analysis 2018   article  
Abstract: The inclusion of the propensity score as a covariate in Bayesian regression trees for causal inference can reduce the bias in treatment effect estimations, which occurs due to the regularization-induced confounding phenomenon. This study advocate for the use of the propensity score by evaluating it under a full-Bayesian variable selection setting, and the use of Individual Conditional Expectation Plots, which is a graphical tool that can improve treatment effect analysis on tree-based Bayesian models and others "black box" models. The first one, even if poorly estimated, can lead to bias reduction on the estimated treatment effects, while the latter can be used to found groups of individuals which have different responses to the applied treatment, and analyze the impact of each variable in the estimated treatment effect.
BibTeX:
@article{Santos2018,
  author = {Santos, Pedro Henrique Filipini dos and Hedibert Freitas Lopes},
  title = {Tree-Based Bayesian Treatment Effect Analysis},
  year = {2018}
}
Ascari, G., Bonomolo, P. and Lopes, H.F. Walk on the Wild Side: Temporarily Unstable Paths and Multiplicative Sunspots 2019 American Economic Review
Vol. 109(5), pp. 1805-1842 
article DOI  
BibTeX:
@article{Ascari2019,
  author = {Guido Ascari and Paolo Bonomolo and Hedibert F. Lopes},
  title = {Walk on the Wild Side: Temporarily Unstable Paths and Multiplicative Sunspots},
  journal = {American Economic Review},
  publisher = {American Economic Association},
  year = {2019},
  volume = {109},
  number = {5},
  pages = {1805--1842},
  doi = {https://doi.org/10.1257/aer.20160576}
}
Berrett, C., Christensen, W.F., Sain, S.R., Sandholtz, N., Coats, D.W., Tebaldi, C. and Lopes, H.F. Modeling sea-level processes on the U.S. Atlantic Coast 2019 Environmetrics
Vol. 31(4) 
article DOI  
BibTeX:
@article{Berrett2019,
  author = {Candace Berrett and William F. Christensen and Stephan R. Sain and Nathan Sandholtz and David W. Coats and Claudia Tebaldi and Hedibert F. Lopes},
  title = {Modeling sea-level processes on the U.S. Atlantic Coast},
  journal = {Environmetrics},
  publisher = {Wiley},
  year = {2019},
  volume = {31},
  number = {4},
  doi = {https://doi.org/10.1002/env.2609}
}
Lopes, H.F. and Polson, N.G. Bayesian hypothesis testing: Redux 2019 Brazilian Journal of Probability and Statistics
Vol. 33(4), pp. 745-755 
article DOI  
BibTeX:
@article{Lopes2019,
  author = {Hedibert F. Lopes and Nicholas G. Polson},
  title = {Bayesian hypothesis testing: Redux},
  journal = {Brazilian Journal of Probability and Statistics},
  publisher = {Institute of Mathematical Statistics},
  year = {2019},
  volume = {33},
  number = {4},
  pages = {745--755},
  doi = {https://doi.org/10.1214/19-bjps442}
}
Lopes, H., Taddy, M. and Gardner, M. Semi-parametric inference for the means of heavy-tailed distributions 2019 Advances in Econometrics: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling, Part B
Vol. 40 
article  
BibTeX:
@article{Lopes2019a,
  author = {Hedibert Lopes and Matthew Taddy and Matthew Gardner},
  title = {Semi-parametric inference for the means of heavy-tailed distributions},
  journal = {Advances in Econometrics: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling, Part B},
  year = {2019},
  volume = {40}
}
Schmidt, A.M. and Lopes, H.F. Dynamic models 2019 Handbook of Environmental and Ecological Statistics, pp. 57-80  inbook  
BibTeX:
@inbook{Schmidt2019,
  author = {Alexandra M. Schmidt and Hedibert F. Lopes},
  title = {Dynamic models},
  booktitle = {Handbook of Environmental and Ecological Statistics},
  publisher = {Chapman & Hall},
  year = {2019},
  pages = {57--80}
}
Virbickaitė, A. and Lopes, H.F. Bayesian semiparametric Markov switching stochastic volatility model 2019 Applied Stochastic Models in Business and Industry
Vol. 35(4), pp. 978-997 
article DOI  
BibTeX:
@article{Virbickaite2019,
  author = {Audronė Virbickaitė and Hedibert F. Lopes},
  title = {Bayesian semiparametric Markov switching stochastic volatility model},
  journal = {Applied Stochastic Models in Business and Industry},
  publisher = {Wiley},
  year = {2019},
  volume = {35},
  number = {4},
  pages = {978--997},
  doi = {https://doi.org/10.1002/asmb.2434}
}
Virbickaitė, A., Lopes, H.F., Ausín, M.C. and Galeano, P. Particle learning for Bayesian semi-parametric stochastic volatility model 2019 Econometric Reviews
Vol. 38(9), pp. 1007-1023 
article DOI  
BibTeX:
@article{Virbickaite2019a,
  author = {Audronė Virbickaitė and Hedibert F. Lopes and M. Concepción Ausín and Pedro Galeano},
  title = {Particle learning for Bayesian semi-parametric stochastic volatility model},
  journal = {Econometric Reviews},
  publisher = {Informa UK Limited},
  year = {2019},
  volume = {38},
  number = {9},
  pages = {1007--1023},
  doi = {https://doi.org/10.1080/07474938.2018.1514022}
}
Bolfarine, H., Carvalho, C.M., Lopes, H.F. and Murray, J.S. Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis 2020   article  
Abstract: Factor Analysis is a popular method for modeling dependence in multivariate data. However, determining the number of factors and obtaining a sparse orientation of the loadings are still major challenges. In this paper, we propose a decision-theoretic approach that brings to light the relation between a sparse representation of the loadings and factor dimension. This relation is done through a summary from information contained in the multivariate posterior. To construct such summary, we introduce a three-step approach. In the first step, the model is fitted with a conservative factor dimension. In the second step, a series of sparse point-estimates, with a decreasing number of factors, is obtained by minimizing an expected predictive loss function. In step three, the degradation in utility in relation to the sparse loadings and factor dimensions is displayed in the posterior summary. The findings are illustrated with applications in classical data from the Factor Analysis literature. We used different prior choices and factor dimensions to demonstrate the flexibility of the proposed method.
BibTeX:
@article{Bolfarine2020,
  author = {Henrique Bolfarine and Carlos M. Carvalho and Hedibert F. Lopes and Jared S. Murray},
  title = {Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis},
  year = {2020}
}
Fava, B. and Lopes, H.F. The Illusion of the Illusion of Sparsity: An exercise in prior sensitivity 2020   article  
Abstract: The emergence of Big Data raises the question of how to model economic relations when there is a large number of possible explanatory variables. We revisit the issue by comparing the possibility of using dense or sparse models in a Bayesian approach, allowing for variable selection and shrinkage. More specifically, we discuss the results reached by Giannone, Lenza, and Primiceri (2020) through a "Spike-and-Slab" prior, which suggest an "illusion of sparsity" in economic data, as no clear patterns of sparsity could be detected. We make a further revision of the posterior distributions of the model, and propose three experiments to evaluate the robustness of the adopted prior distribution. We find that the pattern of sparsity is sensitive to the prior distribution of the regression coefficients, and present evidence that the model indirectly induces variable selection and shrinkage, which suggests that the "illusion of sparsity" could be, itself, an illusion. Code is available on github.com/bfava/IllusionOfIllusion.
BibTeX:
@article{Fava2020,
  author = {Bruno Fava and Hedibert F. Lopes},
  title = {The Illusion of the Illusion of Sparsity: An exercise in prior sensitivity},
  year = {2020}
}
Graziadei, H., Lijoi, A., Lopes, H.F., Paulo C. Marques, F. and Prünster, I. Prior Sensitivity Analysis in a Semi-Parametric Integer-Valued Time Series Model 2020 Entropy
Vol. 22(1), pp. 69 
article DOI  
BibTeX:
@article{Graziadei2020,
  author = {Helton Graziadei and Antonio Lijoi and Hedibert F. Lopes and Paulo C. Marques F. and Igor Prünster},
  title = {Prior Sensitivity Analysis in a Semi-Parametric Integer-Valued Time Series Model},
  journal = {Entropy},
  publisher = {MDPI AG},
  year = {2020},
  volume = {22},
  number = {1},
  pages = {69},
  doi = {https://doi.org/10.3390/e22010069}
}
Paulo C. Marques, F., Graziadei, H. and Lopes, H.F. Bayesian generalizations of the integer-valued autoregressive model 2020 Journal of Applied Statistics, pp. 1-21  article DOI  
BibTeX:
@article{PauloC.Marques2020,
  author = {Paulo C. Marques F. and Helton Graziadei and Hedibert F. Lopes},
  title = {Bayesian generalizations of the integer-valued autoregressive model},
  journal = {Journal of Applied Statistics},
  publisher = {Informa UK Limited},
  year = {2020},
  pages = {1--21},
  doi = {https://doi.org/10.1080/02664763.2020.1812544}
}
Uribe, P.W. and Lopes, H.F. Dynamic sparsity on dynamic regression models 2020   article  
Abstract: In the present work, we consider variable selection and shrinkage for the Gaussian dynamic linear regression within a Bayesian framework. In particular, we propose a novel method that allows for time-varying sparsity, based on an extension of spike-and-slab priors for dynamic models. This is done by assigning appropriate Markov switching priors for the time-varying coefficients' variances, extending the previous work of Ishwaran and Rao (2005). Furthermore, we investigate different priors, including the common Inverted gamma prior for the process variances, and other mixture prior distributions such as Gamma priors for both the spike and the slab, which leads to a mixture of Normal-Gammas priors (Griffin ad Brown, 2010) for the coefficients. In this sense, our prior can be view as a dynamic variable selection prior which induces either smoothness (through the slab) or shrinkage towards zero (through the spike) at each time point. The MCMC method used for posterior computation uses Markov latent variables that can assume binary regimes at each time point to generate the coefficients' variances. In that way, our model is a dynamic mixture model, thus, we could use the algorithm of Gerlach et al (2000) to generate the latent processes without conditioning on the states. Finally, our approach is exemplified through simulated examples and a real data application.
BibTeX:
@article{Uribe2020,
  author = {Paloma W. Uribe and Hedibert F. Lopes},
  title = {Dynamic sparsity on dynamic regression models},
  year = {2020}
}
Levy, B.P.C. and Lopes, H.F. Dynamic Ordering Learning in Multivariate Forecasting 2021   article  
Abstract: In many fields where the main goal is to produce sequential forecasts for decision making problems, the good understanding of the contemporaneous relations among different series is crucial for the estimation of the covariance matrix. In recent years, the modified Cholesky decomposition appeared as a popular approach to covariance matrix estimation. However, its main drawback relies on the imposition of the series ordering structure. In this work, we propose a highly flexible and fast method to deal with the problem of ordering uncertainty in a dynamic fashion with the use of Dynamic Order Probabilities. We apply the proposed method in two different forecasting contexts. The first is a dynamic portfolio allocation problem, where the investor is able to learn the contemporaneous relationships among different currencies improving final decisions and economic performance. The second is a macroeconomic application, where the econometrician can adapt sequentially to new economic environments, switching the contemporaneous relations among macroeconomic variables over time.
BibTeX:
@article{Levy2021,
  author = {Bruno P. C. Levy and Hedibert F. Lopes},
  title = {Dynamic Ordering Learning in Multivariate Forecasting},
  year = {2021}
}