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David Rossell

Personal Details

First Name:David
Middle Name:
Last Name:Rossell
Suffix:
RePEc Short-ID:pro1395
[This author has chosen not to make the email address public]
https://sites.google.com/site/rosselldavid/

Affiliation

Departament d'Economia i Empresa
Universitat Pompeu Fabra
Barcelona School of Economics (BSE)

Barcelona, Spain
http://www.econ.upf.edu/
RePEc:edi:deupfes (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Semken, Christoph & Rossell, David, 2022. "Specification analysis for technology use and teenager well-being: statistical validity and a Bayesian proposal," OSF Preprints cahyq_v1, Center for Open Science.
  2. Jack Jewson & Li Li & Laura Battaglia & Stephen Hansen & David Rossell & Piotr Zwiernik, 2022. "Graphical model inference with external network data," CeMMAP working papers 20/22, Institute for Fiscal Studies.
  3. Semken, Christoph & Rossell, David, 2020. "Bayesian Specification Curve Analysis," OSF Preprints cahyq, Center for Open Science.

Articles

  1. Jack Jewson & David Rossell, 2022. "General Bayesian loss function selection and the use of improper models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1640-1665, November.
  2. Christoph Semken & David Rossell, 2022. "Specification analysis for technology use and teenager well‐being: Statistical validity and a Bayesian proposal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1330-1355, November.
  3. David Rossell & Oriol Abril & Anirban Bhattacharya, 2021. "Approximate Laplace approximations for scalable model selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 853-879, September.
  4. Jairo Fúquene & Mark Steel & David Rossell, 2019. "On choosing mixture components via non‐local priors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(5), pages 809-837, November.
  5. David Rossell & Francisco J. Rubio, 2018. "Tractable Bayesian Variable Selection: Beyond Normality," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1742-1758, October.
  6. O. Papaspiliopoulos & D. Rossell, 2017. "Bayesian block-diagonal variable selection and model averaging," Biometrika, Biometrika Trust, vol. 104(2), pages 343-359.
  7. David Rossell & Donatello Telesca, 2017. "Nonlocal Priors for High-Dimensional Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 254-265, January.
  8. Miquel Duran-Frigola & David Rossell & Patrick Aloy, 2014. "A chemo-centric view of human health and disease," Nature Communications, Nature, vol. 5(1), pages 1-11, December.
  9. Valen E. Johnson & David Rossell, 2012. "Bayesian Model Selection in High-Dimensional Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 649-660, June.
  10. Valen E. Johnson & David Rossell, 2010. "On the use of non‐local prior densities in Bayesian hypothesis tests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 143-170, March.
  11. Rossell David & Guerra Rudy & Scott Clayton, 2008. "Semi-Parametric Differential Expression Analysis via Partial Mixture Estimation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-29, April.

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.

Working papers

    Sorry, no citations of working papers recorded.

Articles

  1. Jack Jewson & David Rossell, 2022. "General Bayesian loss function selection and the use of improper models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1640-1665, November.

    Cited by:

    1. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    2. Akifumi Okuno, 2024. "Minimizing robust density power-based divergences for general parametric density models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(5), pages 851-875, October.

  2. Christoph Semken & David Rossell, 2022. "Specification analysis for technology use and teenager well‐being: Statistical validity and a Bayesian proposal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1330-1355, November.

    Cited by:

    1. Cantone, Giulio Giacomo & Tomaselli, Venera, 2024. "On the Coherence of Composite Indexes: Multiversal Model and Specification Analysis for an Index of Well-Being," MetaArXiv d5y26, Center for Open Science.
    2. Cantone, Giulio Giacomo & Tomaselli, Venera, 2024. "On the Coherence of Composite Indexes: Multiversal Model and Specification Analysis for an Index of Well-Being," MetaArXiv d5y26_v1, Center for Open Science.

  3. David Rossell & Oriol Abril & Anirban Bhattacharya, 2021. "Approximate Laplace approximations for scalable model selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 853-879, September.

    Cited by:

    1. Semken, Christoph & Rossell, David, 2022. "Specification analysis for technology use and teenager well-being: statistical validity and a Bayesian proposal," OSF Preprints cahyq_v1, Center for Open Science.
    2. Christoph Semken & David Rossell, 2022. "Specification analysis for technology use and teenager well‐being: Statistical validity and a Bayesian proposal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1330-1355, November.

  4. Jairo Fúquene & Mark Steel & David Rossell, 2019. "On choosing mixture components via non‐local priors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(5), pages 809-837, November.

    Cited by:

    1. José J. Quinlan & Fernando A. Quintana & Garritt L. Page, 2021. "On a class of repulsive mixture models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 445-461, June.

  5. David Rossell & Francisco J. Rubio, 2018. "Tractable Bayesian Variable Selection: Beyond Normality," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1742-1758, October.

    Cited by:

    1. David Rossell & Oriol Abril & Anirban Bhattacharya, 2021. "Approximate Laplace approximations for scalable model selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 853-879, September.
    2. Zhang, Chun-Xia & Xu, Shuang & Zhang, Jiang-She, 2019. "A novel variational Bayesian method for variable selection in logistic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 1-19.
    3. Francisco J. Rubio Alvarez, 2020. "Letter to the Editor: ‘On Quantile‐based Asymmetric Family of Distributions: Properties and Inference’," International Statistical Review, International Statistical Institute, vol. 88(3), pages 793-796, December.
    4. Jack Jewson & David Rossell, 2022. "General Bayesian loss function selection and the use of improper models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1640-1665, November.
    5. Nadja Klein & Michael Stanley Smith, 2021. "Bayesian variable selection for non‐Gaussian responses: a marginally calibrated copula approach," Biometrics, The International Biometric Society, vol. 77(3), pages 809-823, September.
    6. Mai Dao & Min Wang & Souparno Ghosh & Keying Ye, 2022. "Bayesian variable selection and estimation in quantile regression using a quantile-specific prior," Computational Statistics, Springer, vol. 37(3), pages 1339-1368, July.

  6. O. Papaspiliopoulos & D. Rossell, 2017. "Bayesian block-diagonal variable selection and model averaging," Biometrika, Biometrika Trust, vol. 104(2), pages 343-359.

    Cited by:

    1. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.

  7. David Rossell & Donatello Telesca, 2017. "Nonlocal Priors for High-Dimensional Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 254-265, January.

    Cited by:

    1. Semken, Christoph & Rossell, David, 2022. "Specification analysis for technology use and teenager well-being: statistical validity and a Bayesian proposal," OSF Preprints cahyq_v1, Center for Open Science.
    2. Weibing Li & Thierry Chekouo, 2022. "Bayesian group selection with non-local priors," Computational Statistics, Springer, vol. 37(1), pages 287-302, March.
    3. Zhang, Chun-Xia & Xu, Shuang & Zhang, Jiang-She, 2019. "A novel variational Bayesian method for variable selection in logistic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 1-19.
    4. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    5. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    6. Shi, Guiling & Lim, Chae Young & Maiti, Tapabrata, 2019. "Model selection using mass-nonlocal prior," Statistics & Probability Letters, Elsevier, vol. 147(C), pages 36-44.
    7. Ho-Hsiang Wu & Marco A. R. Ferreira & Mohamed Elkhouly & Tieming Ji, 2020. "Hyper Nonlocal Priors for Variable Selection in Generalized Linear Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 147-185, February.

  8. Valen E. Johnson & David Rossell, 2012. "Bayesian Model Selection in High-Dimensional Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 649-660, June.

    Cited by:

    1. Cao, Xuan & Khare, Kshitij & Ghosh, Malay, 2020. "Consistent Bayesian sparsity selection for high-dimensional Gaussian DAG models with multiplicative and beta-mixture priors," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    2. Minerva Mukhopadhyay & Tapas Samanta, 2017. "A mixture of g-priors for variable selection when the number of regressors grows with the sample size," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 377-404, June.
    3. Weibing Li & Thierry Chekouo, 2022. "Bayesian group selection with non-local priors," Computational Statistics, Springer, vol. 37(1), pages 287-302, March.
    4. Ouyang, Jiarong & Cao, Xuan, 2024. "Consistent skinny Gibbs in probit regression," Computational Statistics & Data Analysis, Elsevier, vol. 198(C).
    5. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
    6. Nelson, Kelly & Brown, Zachary S. & Parton, Lee, 2019. "Biofuels Policy and Innovation Impacts: Evidence from Biofuels and Agricultural Patent Indicators," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 291243, Agricultural and Applied Economics Association.
    7. Fouskakis, Dimitris & Ntzoufras, Ioannis & Perrakis, Konstantinos, 2020. "Variations of power-expected-posterior priors in normal regression models," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    8. Davide Altomare & Guido Consonni & Luca La Rocca, 2013. "Objective Bayesian Search of Gaussian Directed Acyclic Graphical Models for Ordered Variables with Non-Local Priors," Biometrics, The International Biometric Society, vol. 69(2), pages 478-487, June.
    9. Wang, Jia & Cai, Xizhen & Li, Runze, 2021. "Variable selection for partially linear models via Bayesian subset modeling with diffusing prior," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    10. Faming Liang & Momiao Xiong, 2013. "Bayesian Detection of Causal Rare Variants under Posterior Consistency," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-16, July.
    11. Latouche, Pierre & Mattei, Pierre-Alexandre & Bouveyron, Charles & Chiquet, Julien, 2016. "Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 177-190.
    12. Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2017. "A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case study," Biometrics, The International Biometric Society, vol. 73(2), pages 615-624, June.
    13. Andrés Ramírez-Hassan, 2020. "Dynamic variable selection in dynamic logistic regression: an application to Internet subscription," Empirical Economics, Springer, vol. 59(2), pages 909-932, August.
    14. Dimitris Korobilis, 2018. "Machine Learning Macroeconometrics: A Primer," Working Paper series 18-30, Rimini Centre for Economic Analysis.
    15. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    16. Qifan Song & Guang Cheng, 2020. "Bayesian Fusion Estimation via t Shrinkage," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 353-385, August.
    17. Shi, Guiling & Lim, Chae Young & Maiti, Tapabrata, 2019. "Model selection using mass-nonlocal prior," Statistics & Probability Letters, Elsevier, vol. 147(C), pages 36-44.
    18. Andrew J. Womack & Luis León-Novelo & George Casella, 2014. "Inference From Intrinsic Bayes' Procedures Under Model Selection and Uncertainty," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1040-1053, September.
    19. Li, Cheng & Jiang, Wenxin, 2016. "On oracle property and asymptotic validity of Bayesian generalized method of moments," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 132-147.
    20. Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2015. "miRNA–target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer," Biometrics, The International Biometric Society, vol. 71(2), pages 428-438, June.
    21. Minerva Mukhopadhyay & Sourabh Bhattacharya, 2022. "Bayes factor asymptotics for variable selection in the Gaussian process framework," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 581-613, June.
    22. Gonzalo García-Donato & María Eugenia Castellanos & Alicia Quirós, 2021. "Bayesian Variable Selection with Applications in Health Sciences," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    23. Ho-Hsiang Wu & Marco A. R. Ferreira & Mohamed Elkhouly & Tieming Ji, 2020. "Hyper Nonlocal Priors for Variable Selection in Generalized Linear Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 147-185, February.
    24. Xueying Tang & Xiaofan Xu & Malay Ghosh & Prasenjit Ghosh, 2018. "Bayesian Variable Selection and Estimation Based on Global-Local Shrinkage Priors," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 215-246, August.
    25. Shi, Guiling & Lim, Chae Young & Maiti, Tapabrata, 2019. "Bayesian model selection for generalized linear models using non-local priors," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 285-296.
    26. Qifan Song & Faming Liang, 2015. "High-Dimensional Variable Selection With Reciprocal L 1 -Regularization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1607-1620, December.
    27. Anirban Bhattacharya & Debdeep Pati & Natesh S. Pillai & David B. Dunson, 2015. "Dirichlet--Laplace Priors for Optimal Shrinkage," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1479-1490, December.
    28. Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.

  9. Valen E. Johnson & David Rossell, 2010. "On the use of non‐local prior densities in Bayesian hypothesis tests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 143-170, March.

    Cited by:

    1. Fetene B. Tekle & Dereje W. Gudicha & Jeroen K. Vermunt, 2016. "Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models," 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. 10(2), pages 209-224, June.
    2. Gelman Andrew & Robert Christian P. & Rousseau Judith, 2013. "Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin," Statistics & Risk Modeling, De Gruyter, vol. 30(2), pages 105-120, June.
    3. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    4. Weibing Li & Thierry Chekouo, 2022. "Bayesian group selection with non-local priors," Computational Statistics, Springer, vol. 37(1), pages 287-302, March.
    5. Christian P. Robert, 2013. "On the jeffreys-Lindley's Paradox," Working Papers 2013-46, Center for Research in Economics and Statistics.
    6. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
    7. David Rossell & Oriol Abril & Anirban Bhattacharya, 2021. "Approximate Laplace approximations for scalable model selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 853-879, September.
    8. Francesco Denti & Michele Guindani & Fabrizio Leisen & Antonio Lijoi & William Duncan Wadsworth & Marina Vannucci, 2021. "Two‐group Poisson‐Dirichlet mixtures for multiple testing," Biometrics, The International Biometric Society, vol. 77(2), pages 622-633, June.
    9. Davide Altomare & Guido Consonni & Luca La Rocca, 2013. "Objective Bayesian Search of Gaussian Directed Acyclic Graphical Models for Ordered Variables with Non-Local Priors," Biometrics, The International Biometric Society, vol. 69(2), pages 478-487, June.
    10. Eugenio Melilli & Piero Veronese, 2024. "Confidence distributions and hypothesis testing," Statistical Papers, Springer, vol. 65(6), pages 3789-3820, August.
    11. D. Fouskakis, 2019. "Priors via imaginary training samples of sufficient statistics for objective Bayesian hypothesis testing," METRON, Springer;Sapienza Università di Roma, vol. 77(3), pages 179-199, December.
    12. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    13. Nilotpal Sanyal & Marco A. R. Ferreira, 2017. "Bayesian Wavelet Analysis Using Nonlocal Priors with an Application to fMRI Analysis," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(2), pages 361-388, November.
    14. Konstantinos Bourazas & Guido Consonni & Laura Deldossi, 2024. "Bayesian sample size determination for detecting heterogeneity in multi-site replication studies," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(3), pages 697-716, September.
    15. T S Shively & S G Walker, 2018. "On Bayes factors for the linear model," Biometrika, Biometrika Trust, vol. 105(3), pages 739-744.
    16. David Rossell & Donatello Telesca, 2017. "Nonlocal Priors for High-Dimensional Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 254-265, January.
    17. Alexander Ly & Eric-Jan Wagenmakers, 2022. "Bayes factors for peri-null hypotheses," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1121-1142, December.
    18. D. Vélez & M. E. Pérez & L. R. Pericchi, 2022. "Increasing the replicability for linear models via adaptive significance levels," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 771-789, September.
    19. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    20. Shi, Guiling & Lim, Chae Young & Maiti, Tapabrata, 2019. "Model selection using mass-nonlocal prior," Statistics & Probability Letters, Elsevier, vol. 147(C), pages 36-44.
    21. Li, Hanning & Pati, Debdeep, 2017. "Variable selection using shrinkage priors," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 107-119.
    22. Kelter, Riko, 2022. "Power analysis and type I and type II error rates of Bayesian nonparametric two-sample tests for location-shifts based on the Bayes factor under Cauchy priors," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    23. Ho-Hsiang Wu & Marco A. R. Ferreira & Mohamed Elkhouly & Tieming Ji, 2020. "Hyper Nonlocal Priors for Variable Selection in Generalized Linear Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 147-185, February.
    24. Laura D'Angelo & Antonio Canale & Zhaoxia Yu & Michele Guindani, 2023. "Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data," Biometrics, The International Biometric Society, vol. 79(2), pages 1370-1382, June.
    25. Arnab Kumar Maity & Sanjib Basu & Santu Ghosh, 2021. "Bayesian criterion‐based variable selection," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 835-857, August.
    26. Scott D. Goddard & Valen E. Johnson, 2016. "Restricted most powerful Bayesian tests for linear models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1162-1177, December.
    27. David G Jenkins & Pedro F Quintana-Ascencio, 2020. "A solution to minimum sample size for regressions," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-15, February.
    28. Christoph Semken & David Rossell, 2022. "Specification analysis for technology use and teenager well‐being: Statistical validity and a Bayesian proposal," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1330-1355, November.
    29. Shi, Guiling & Lim, Chae Young & Maiti, Tapabrata, 2019. "Bayesian model selection for generalized linear models using non-local priors," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 285-296.
    30. Guido Consonni & Luca La Rocca, 2010. "Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs," Quaderni di Dipartimento 115, University of Pavia, Department of Economics and Quantitative Methods.
    31. Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.

More information

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Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (1) 2022-12-12
  2. NEP-NET: Network Economics (1) 2022-12-12

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