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Convex Optimization in R

Citations

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Cited by:

  1. Feng, Long & Dicker, Lee H., 2018. "Approximate nonparametric maximum likelihood for mixture models: A convex optimization approach to fitting arbitrary multivariate mixing distributions," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 80-91.
  2. Jiaying Gu & Roger Koenker, 2021. "Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis," Papers 2112.11064, arXiv.org.
  3. Chen, Kun & Zhu, Joe, 2019. "Computational tractability of chance constrained data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1037-1046.
  4. Timothy B. Armstrong & Michal Kolesár & Mikkel Plagborg‐Møller, 2022. "Robust Empirical Bayes Confidence Intervals," Econometrica, Econometric Society, vol. 90(6), pages 2567-2602, November.
  5. Wang, Yihe & Zhao, Sihai Dave, 2021. "A nonparametric empirical Bayes approach to large-scale multivariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
  6. Sihai Dave Zhao, 2017. "Integrative genetic risk prediction using non-parametric empirical Bayes classification," Biometrics, The International Biometric Society, vol. 73(2), pages 582-592, June.
  7. Jiaying Gu & Roger Koenker, 2014. "Unobserved heterogeneity in income dynamics: an empirical Bayes perspective," CeMMAP working papers CWP43/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  8. Zhan Gao & Zhentao Shi, 2021. "Implementing Convex Optimization in R: Two Econometric Examples," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1127-1135, December.
  9. Fox, Jeremy T. & Kim, Kyoo il & Yang, Chenyu, 2016. "A simple nonparametric approach to estimating the distribution of random coefficients in structural models," Journal of Econometrics, Elsevier, vol. 195(2), pages 236-254.
  10. Geoffrey Brent, 2018. "Maximum likelihood estimation framework for table‐balancing adjustments," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 520-532, November.
  11. Sokbae Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2018. "Oracle Estimation of a Change Point in High-Dimensional Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1184-1194, July.
  12. Alberto Abadie & Maximilian Kasy, 2019. "Choosing Among Regularized Estimators in Empirical Economics: The Risk of Machine Learning," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 743-762, December.
  13. Chen, Kun & Zhu, Joe, 2017. "Second order cone programming approach to two-stage network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 262(1), pages 231-238.
  14. Li, Xiaoou & Chen, Yunxiao & Chen, Xi & Liu, Jingchen & Ying, Zhiliang, 2021. "Optimal stopping and worker selection in crowdsourcing: an adaptive sequential probability ratio test framework," LSE Research Online Documents on Economics 100873, London School of Economics and Political Science, LSE Library.
  15. Jiaying Gu & Roger Koenker, 2017. "Rebayes: an R package for empirical bayes mixture methods," CeMMAP working papers CWP37/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  16. Jiaying Gu & Roger Koenker & Stanislav Volgushev, 2017. "Testing for homogeneity in mixture models," CeMMAP working papers 39/17, Institute for Fiscal Studies.
  17. Roger Koenker, 2017. "Bayesian deconvolution: an R vinaigrette," CeMMAP working papers CWP38/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  18. Jiaying Gu & Roger Koenker, 2014. "Unobserved heterogeneity in income dynamics: an empirical Bayes perspective," CeMMAP working papers 43/14, Institute for Fiscal Studies.
  19. Mike Gilraine & Jiaying Gu & Robert McMillan, 2022. "A Nonparametric Approach for Studying Teacher Impacts," Working Papers tecipa-716, University of Toronto, Department of Economics.
  20. Jiafeng Chen, 2022. "Empirical Bayes When Estimation Precision Predicts Parameters," Papers 2212.14444, arXiv.org, revised Apr 2024.
  21. Adrian Gepp & Geoff Harris & Bruce Vanstone, 2020. "Financial applications of semidefinite programming: a review and call for interdisciplinary research," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3527-3555, December.
  22. Kara Karpman & Samriddha Lahiry & Diganta Mukherjee & Sumanta Basu, 2022. "Exploring Financial Networks Using Quantile Regression and Granger Causality," Papers 2207.10705, arXiv.org, revised Jul 2022.
  23. Park, Junyong, 2018. "Simultaneous estimation based on empirical likelihood and general maximum likelihood estimation," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 19-31.
  24. Michael Gilraine & Jiaying Gu & Robert McMillan, 2020. "A New Method for Estimating Teacher Value-Added," NBER Working Papers 27094, National Bureau of Economic Research, Inc.
  25. Jiaying Gu & Roger Koenker, 2017. "Rebayes: an R package for empirical bayes mixture methods," CeMMAP working papers 37/17, Institute for Fiscal Studies.
  26. Li Tan & Cory Koedel, 2019. "The Effects of Differential Income Replacement and Mortality on U.S. Social Security Redistribution," Southern Economic Journal, John Wiley & Sons, vol. 86(2), pages 613-637, October.
  27. Jiaying Gu & Roger Koenker, 2018. "Nonparametric maximum likelihood methods for binary response models with random coefficients," Papers 1811.03329, arXiv.org, revised Jan 2020.
  28. St'ephane Bonhomme & Martin Weidner, 2019. "Posterior Average Effects," Papers 1906.06360, arXiv.org, revised Sep 2021.
  29. Timothy B. Armstrong & Michal Koles'ar & Mikkel Plagborg-M{o}ller, 2020. "Robust Empirical Bayes Confidence Intervals," Papers 2004.03448, arXiv.org, revised May 2022.
  30. Martina Hančová & Andrej Gajdoš & Jozef Hanč & Gabriela Vozáriková, 2021. "Estimating variances in time series kriging using convex optimization and empirical BLUPs," Statistical Papers, Springer, vol. 62(4), pages 1899-1938, August.
  31. Roger Koenker & Jiaying Gu, 2019. "Minimalist G-modelling: A comment on Efron," CeMMAP working papers CWP13/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  32. Michael Gilraine & Jiaying Gu & Robert McMillan, 2021. "A Nonparametric Method for Estimating Teacher Value-Added," Working Papers tecipa-689, University of Toronto, Department of Economics.
  33. Jiaying Gu & Roger Koenker & Stanislav Volgushev, 2017. "Testing for homogeneity in mixture models," CeMMAP working papers CWP39/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  34. Xinyi Zhong & Chang Su & Zhou Fan, 2022. "Empirical Bayes PCA in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 853-878, July.
  35. Lina Liao & Cheolwoo Park & Hosik Choi, 2019. "Penalized expectile regression: an alternative to penalized quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 409-438, April.
  36. Jiaying Gu & Roger Koenker, 2020. "Invidious Comparisons: Ranking and Selection as Compound Decisions," Papers 2012.12550, arXiv.org, revised Sep 2021.
  37. Roger Koenker, 2017. "Bayesian deconvolution: an R vinaigrette," CeMMAP working papers 38/17, Institute for Fiscal Studies.
  38. Jiaying Gu & Roger Koenker, 2018. "Nonparametric maximum likelihood methods for binary response models with random coefficients," CeMMAP working papers CWP65/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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