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Generating random correlation matrices based on partial correlations

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Cited by:

  1. Ng, Chi Tim & Joe, Harry, 2010. "Generating random AR(p) and MA(q) Toeplitz correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1532-1545, July.
  2. Ilya Archakov & Peter Reinhard Hansen & Yiyao Luo, 2022. "A New Method for Generating Random Correlation Matrices," Papers 2210.08147, arXiv.org.
  3. Davide Delle Monache & Ivan Petrella & Fabrizio Venditti, 2021. "Price Dividend Ratio and Long-Run Stock Returns: A Score-Driven State Space Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1054-1065, October.
  4. Michael S. Smith & Shaun P. Vahey, 2016. "Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 416-434, July.
  5. Soyeon Ahn & John M. Abbamonte, 2020. "A new approach for handling missing correlation values for meta‐analytic structural equation modeling: Corboundary R package," Campbell Systematic Reviews, John Wiley & Sons, vol. 16(1), March.
  6. Daniels, M.J. & Pourahmadi, M., 2009. "Modeling covariance matrices via partial autocorrelations," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2352-2363, November.
  7. Joseph Romano & Azeem Shaikh & Michael Wolf, 2008. "Control of the false discovery rate under dependence using the bootstrap and subsampling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 417-442, November.
  8. Böhm, Walter & Hornik, Kurt, 2014. "Generating random correlation matrices by the simple rejection method: Why it does not work," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 27-30.
  9. Hirofumi Michimae & Takeshi Emura, 2022. "Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients," Computational Statistics, Springer, vol. 37(5), pages 2741-2769, November.
  10. Smith, Michael Stanley, 2015. "Copula modelling of dependence in multivariate time series," International Journal of Forecasting, Elsevier, vol. 31(3), pages 815-833.
  11. Sylvia Gottschalk, 2016. "Entropy and credit risk in highly correlated markets," Papers 1604.07042, arXiv.org.
  12. Jean-Claude Hessing & Rutger-Jan Lange & Daniel Ralph, 2022. "This article establishes the Poisson optional stopping times (POST) method by Lange et al. (2020) as a near-universal method for solving liquidity-constrained American options, or, equivalently, penal," Tinbergen Institute Discussion Papers 22-007/IV, Tinbergen Institute.
  13. Tuitman, Jan & Vanduffel, Steven & Yao, Jing, 2020. "Correlation matrices with average constraints," Statistics & Probability Letters, Elsevier, vol. 165(C).
  14. Madar, Vered, 2015. "Direct formulation to Cholesky decomposition of a general nonsingular correlation matrix," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 142-147.
  15. Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
  16. Brian Hartley, 2020. "Corridor stability of the Kaleckian growth model: a Markov-switching approach," Working Papers 2013, New School for Social Research, Department of Economics, revised Nov 2020.
  17. Bladt Martin & McNeil Alexander J., 2022. "Time series with infinite-order partial copula dependence," Dependence Modeling, De Gruyter, vol. 10(1), pages 87-107, January.
  18. Heisig, Jan Paul & Schaeffer, Merlin & Giesecke, Johannes, 2017. "The Costs of Simplicity: Why Multilevel Models May Benefit from Accounting for Cross-Cluster Differences in the Effects of Controls," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 82(4), pages 796-827.
  19. Lai, Yuanhao & McLeod, Ian, 2020. "Ensemble quantile classifier," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  20. Hui Yao & Sungduk Kim & Ming-Hui Chen & Joseph G. Ibrahim & Arvind K. Shah & Jianxin Lin, 2015. "Bayesian Inference for Multivariate Meta-Regression With a Partially Observed Within-Study Sample Covariance Matrix," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 528-544, June.
  21. Benjamin Poignard & Jean-Davis Fermanian, 2014. "Dynamic Asset Correlations Based on Vines," Working Papers 2014-46, Center for Research in Economics and Statistics.
  22. Steffen Liebscher & Thomas Kirschstein, 2015. "Efficiency of the pMST and RDELA location and scatter estimators," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 63-82, January.
  23. Wang, Y. & Daniels, M.J., 2013. "Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 130-140.
  24. Durante Fabrizio & Puccetti Giovanni & Scherer Matthias & Vanduffel Steven, 2017. "The Vine Philosopher: An interview with Roger Cooke," Dependence Modeling, De Gruyter, vol. 5(1), pages 256-267, December.
  25. Flórez, Alvaro J. & Molenberghs, Geert & Van der Elst, Wim & Alonso Abad, Ariel, 2022. "An efficient algorithm to assess multivariate surrogate endpoints in a causal inference framework," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
  26. Azamir, Bouchaib & Bennis, Driss & Michel, Bertrand, 2022. "A simplified algorithm for identifying abnormal changes in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
  27. Kirschstein, Thomas & Liebscher, Steffen & Becker, Claudia, 2013. "Robust estimation of location and scatter by pruning the minimum spanning tree," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 173-184.
  28. Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Mar 2024.
  29. Falk, Carl F. & Muthukrishna, Michael, 2021. "Parsimony in model selection: tools for assessing fit propensity," LSE Research Online Documents on Economics 110856, London School of Economics and Political Science, LSE Library.
  30. Kossova, Elena & Potanin, Bogdan, 2018. "Heckman method and switching regression model multivariate generalization," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 50, pages 114-143.
  31. Trucíos, Carlos & Hotta, Luiz K. & Valls Pereira, Pedro L., 2019. "On the robustness of the principal volatility components," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 201-219.
  32. Brian Hartley, 2022. "Episodic incidence of Harrodian instability and the Kaleckian growth model: A Markov‐switching approach," Metroeconomica, Wiley Blackwell, vol. 73(1), pages 268-290, February.
  33. Saxena, Shobhit & Bhat, Chandra R. & Pinjari, Abdul Rawoof, 2023. "Separation-based parameterization strategies for estimation of restricted covariance matrices in multivariate model systems," Journal of choice modelling, Elsevier, vol. 47(C).
  34. Pourahmadi, Mohsen & Wang, Xiao, 2015. "Distribution of random correlation matrices: Hyperspherical parameterization of the Cholesky factor," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 5-12.
  35. Kossova, Elena & Kupriianova, Liubov & Potanin, Bogdan, 2020. "Parametric and semiparametric multivariate sample selection models estimators’ accuracy: Comparative analysis on simulated data," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 119-139.
  36. Sacha Epskamp & Adela-Maria Isvoranu & Mike W.-L. Cheung, 2022. "Meta-analytic Gaussian Network Aggregation," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 12-46, March.
  37. Kurowicka, Dorota, 2014. "Joint density of correlations in the correlation matrix with chordal sparsity patterns," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 160-170.
  38. Christopher J. Bennett, 2009. "p-Value Adjustments for Asymptotic Control of the Generalized Familywise Error Rate," Vanderbilt University Department of Economics Working Papers 0905, Vanderbilt University Department of Economics.
  39. Martin Bladt & Alexander J. McNeil, 2021. "Time series models with infinite-order partial copula dependence," Papers 2107.00960, arXiv.org.
  40. Forrester, Peter J. & Zhang, Jiyuan, 2020. "Parametrising correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
  41. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  42. Oh Kang Kwon & Stephen Satchell, 2021. "Treating cross‐sectional and time series momentum returns as forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 834-848, August.
  43. Gottschalk, Sylvia, 2017. "Entropy measure of credit risk in highly correlated markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 11-19.
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