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Convex Optimization in R

Citations

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Cited by:

  1. Jiaying Gu & Roger Koenker, 2021. "Ranking and Selection from Pairwise Comparisons: Empirical Bayes Methods for Citation Analysis," Papers 2112.11064, arXiv.org.
  2. Timothy B. Armstrong & Michal Kolesár & Mikkel Plagborg‐Møller, 2022. "Robust Empirical Bayes Confidence Intervals," Econometrica, Econometric Society, vol. 90(6), pages 2567-2602, November.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Jiaying Gu & Roger Koenker & Stanislav Volgushev, 2017. "Testing for homogeneity in mixture models," CeMMAP working papers 39/17, Institute for Fiscal Studies.
  10. Jiaying Gu & Roger Koenker, 2014. "Unobserved heterogeneity in income dynamics: an empirical Bayes perspective," CeMMAP working papers 43/14, Institute for Fiscal Studies.
  11. Mike Gilraine & Jiaying Gu & Robert McMillan, 2022. "A Nonparametric Approach for Studying Teacher Impacts," Working Papers tecipa-716, University of Toronto, Department of Economics.
  12. 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.
  13. 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.
  14. St'ephane Bonhomme & Martin Weidner, 2019. "Posterior Average Effects," Papers 1906.06360, arXiv.org, revised Sep 2021.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Roger Koenker, 2017. "Bayesian deconvolution: an R vinaigrette," CeMMAP working papers 38/17, Institute for Fiscal Studies.
  20. 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.
  21. 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.
  22. 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.
  23. Wang, Yihe & Zhao, Sihai Dave, 2021. "A nonparametric empirical Bayes approach to large-scale multivariate regression," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
  24. 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.
  25. 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.
  26. 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.
  27. Roger Koenker, 2017. "Bayesian deconvolution: an R vinaigrette," CeMMAP working papers CWP38/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  28. Jiafeng Chen, 2022. "Empirical Bayes When Estimation Precision Predicts Parameters," Papers 2212.14444, arXiv.org, revised Apr 2024.
  29. 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.
  30. 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.
  31. Jiaying Gu & Roger Koenker, 2017. "Rebayes: an R package for empirical bayes mixture methods," CeMMAP working papers 37/17, Institute for Fiscal Studies.
  32. 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.
  33. Jiaying Gu & Roger Koenker, 2018. "Nonparametric maximum likelihood methods for binary response models with random coefficients," Papers 1811.03329, arXiv.org, revised Jan 2020.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. Jiaying Gu & Roger Koenker, 2020. "Invidious Comparisons: Ranking and Selection as Compound Decisions," Papers 2012.12550, arXiv.org, revised Sep 2021.
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