1% Verified bibliography for UQO WP28 (literature/PRIOR_ART_MATRIX.csv records how each entry was checked).2@article{zhang2013communication,3 author = {Zhang, Yuchen and Duchi, John C. and Wainwright, Martin J.},4 title = {Communication-Efficient Algorithms for Statistical Optimization},5 journal = {Journal of Machine Learning Research},6 year = {2013}, volume = {14}, pages = {3321--3363},7 url = {https://www.jmlr.org/papers/v14/zhang13b.html}8}9@article{huang2019distributed,10 author = {Huang, Cheng and Huo, Xiaoming},11 title = {A Distributed One-Step Estimator},12 journal = {Mathematical Programming},13 year = {2019}, volume = {174}, number = {1--2}, pages = {41--76},14 doi = {10.1007/s10107-019-01369-0}15}16@article{dhaene2015split,17 author = {Dhaene, Geert and Jochmans, Koen},18 title = {Split-Panel Jackknife Estimation of Fixed-Effect Models},19 journal = {Review of Economic Studies},20 year = {2015}, volume = {82}, number = {3}, pages = {991--1030},21 doi = {10.1093/restud/rdv007}22}23@article{jordan2019communication,24 author = {Jordan, Michael I. and Lee, Jason D. and Yang, Yun},25 title = {Communication-Efficient Distributed Statistical Inference},26 journal = {Journal of the American Statistical Association},27 year = {2019}, volume = {114}, number = {526}, pages = {668--681},28 doi = {10.1080/01621459.2018.1429274}29}30@article{fan2023communication,31 author = {Fan, Jianqing and Guo, Yongyi and Wang, Kaizheng},32 title = {Communication-Efficient Accurate Statistical Estimation},33 journal = {Journal of the American Statistical Association},34 year = {2023}, volume = {118}, number = {542}, pages = {1000--1010},35 doi = {10.1080/01621459.2021.1969238}36}37@article{rosenblatt2016optimality,38 author = {Rosenblatt, Jonathan D. and Nadler, Boaz},39 title = {On the Optimality of Averaging in Distributed Statistical Learning},40 journal = {Information and Inference: A Journal of the IMA},41 year = {2016}, volume = {5}, number = {4}, pages = {379--404},42 doi = {10.1093/imaiai/iaw013}43}44@article{lin2011aggregated,45 author = {Lin, Nan and Xi, Ruibin},46 title = {Aggregated Estimating Equation Estimation},47 journal = {Statistics and Its Interface},48 year = {2011}, volume = {4}, number = {1}, pages = {73--83},49 doi = {10.4310/SII.2011.v4.n1.a8}50}51@article{battey2018distributed,52 author = {Battey, Heather and Fan, Jianqing and Liu, Han and Lu, Junwei and Zhu, Ziwei},53 title = {Distributed Testing and Estimation under Sparse High Dimensional Models},54 journal = {Annals of Statistics},55 year = {2018}, volume = {46}, number = {3}, pages = {1352--1382},56 doi = {10.1214/17-AOS1587}57}58@article{volgushev2019distributed,59 author = {Volgushev, Stanislav and Chao, Shih-Kang and Cheng, Guang},60 title = {Distributed Inference for Quantile Regression Processes},61 journal = {Annals of Statistics},62 year = {2019}, volume = {47}, number = {3}, pages = {1634--1662},63 doi = {10.1214/18-AOS1730}64}65@article{shang2017computational,66 author = {Shang, Zuofeng and Cheng, Guang},67 title = {Computational Limits of a Distributed Algorithm for Smoothing Spline},68 journal = {Journal of Machine Learning Research},69 year = {2017}, volume = {18}, number = {108}, pages = {1--37},70 url = {https://jmlr.org/papers/v18/16-289.html}71}72@inproceedings{shamir2014communication,73 author = {Shamir, Ohad and Srebro, Nathan and Zhang, Tong},74 title = {Communication-Efficient Distributed Optimization Using an Approximate {N}ewton-Type Method},75 booktitle = {Proceedings of the 31st International Conference on Machine Learning},76 series = {Proceedings of Machine Learning Research}, volume = {32}, number = {2}, pages = {1000--1008},77 year = {2014}, publisher = {PMLR}78}79@inproceedings{wang2017efficient,80 author = {Wang, Jialei and Kolar, Mladen and Srebro, Nathan and Zhang, Tong},81 title = {Efficient Distributed Learning with Sparsity},82 booktitle = {Proceedings of the 34th International Conference on Machine Learning},83 series = {Proceedings of Machine Learning Research}, volume = {70}, pages = {3636--3645},84 year = {2017}, publisher = {PMLR}85}86@article{chen2022first,87 author = {Chen, Xi and Liu, Weidong and Zhang, Yichen},88 title = {First-Order {N}ewton-Type Estimator for Distributed Estimation and Inference},89 journal = {Journal of the American Statistical Association},90 year = {2022}, volume = {117}, number = {540}, pages = {1858--1874},91 doi = {10.1080/01621459.2021.1891925}92}93@article{zhu2021least,94 author = {Zhu, Xuening and Li, Feng and Wang, Hansheng},95 title = {Least-Square Approximation for a Distributed System},96 journal = {Journal of Computational and Graphical Statistics},97 year = {2021}, volume = {30}, number = {4}, pages = {1004--1018},98 doi = {10.1080/10618600.2021.1923517}99}100@article{gu2023heterogeneity,101 author = {Gu, Jia and Chen, Songxi},102 title = {Distributed Statistical Inference under Heterogeneity},103 journal = {Journal of Machine Learning Research},104 year = {2023}, volume = {24}, number = {387}, pages = {1--56},105 url = {https://jmlr.org/papers/v24/22-1274.html}106}107@article{wu2023subsampling,108 author = {Wu, Shuyuan and Zhu, Xuening and Wang, Hansheng},109 title = {Subsampling and Jackknifing: A Practically Convenient Solution for Large Data Analysis with Limited Computational Resources},110 journal = {Statistica Sinica},111 year = {2023}, volume = {33}, pages = {2041--2064},112 doi = {10.5705/ss.202021.0257}113}114@unpublished{yin2022newton,115 author = {Yin, Zhuoran},116 title = {Communication-Efficient Distributed {N}ewton-Like Optimization with Gradients and {M}-Estimators},117 note = {arXiv:2207.06253},118 year = {2022}119}120@article{gao2022review,121 author = {Gao, Yuan and Liu, Weidong and Wang, Hansheng and Wang, Xiaozhou and Yan, Yibo and Zhang, Riquan},122 title = {A Review of Distributed Statistical Inference},123 journal = {Statistical Theory and Related Fields},124 year = {2022}, volume = {6}, number = {2}, pages = {89--99},125 doi = {10.1080/24754269.2021.1974158}126}127@unpublished{li2024selective,128 author = {Li, Xuetong and Gao, Yuan and Chang, Hong and Huang, Danyang and Ma, Yingying and Pan, Rui and Qi, Haobo and Wang, Feifei and Wu, Shuyuan and Xu, Ke and Zhou, Jing and Zhu, Xuening and Zhu, Yingqiu and Wang, Hansheng},129 title = {A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques},130 note = {arXiv:2403.11163},131 year = {2024}132}133@article{hahn2024efficient,134 author = {Hahn, Jinyong and Hughes, David W. and Kuersteiner, Guido and Newey, Whitney K.},135 title = {Efficient Bias Correction for Cross-Section and Panel Data},136 journal = {Quantitative Economics},137 year = {2024}, volume = {15}, number = {3}, pages = {783--816},138 doi = {10.3982/QE2350}139}140@article{quenouille1949approximate,141 author = {Quenouille, M. H.},142 title = {Approximate Tests of Correlation in Time-Series},143 journal = {Journal of the Royal Statistical Society: Series B},144 year = {1949}, volume = {11}, number = {1}, pages = {68--84},145 doi = {10.1111/j.2517-6161.1949.tb00023.x}146}147@article{quenouille1956notes,148 author = {Quenouille, M. H.},149 title = {Notes on Bias in Estimation},150 journal = {Biometrika},151 year = {1956}, volume = {43}, number = {3/4}, pages = {353--360},152 doi = {10.1093/biomet/43.3-4.353}153}154@article{schucany1971bias,155 author = {Schucany, W. R. and Gray, H. L. and Owen, D. B.},156 title = {On Bias Reduction in Estimation},157 journal = {Journal of the American Statistical Association},158 year = {1971}, volume = {66}, number = {335}, pages = {524--533},159 doi = {10.1080/01621459.1971.10482296}160}161@book{efron1982jackknife,162 author = {Efron, Bradley},163 title = {The Jackknife, the Bootstrap and Other Resampling Plans},164 publisher = {SIAM}, address = {Philadelphia}, year = {1982},165 series = {CBMS-NSF Regional Conference Series in Applied Mathematics}, number = {38},166 doi = {10.1137/1.9781611970319}167}168@article{cox1968general,169 author = {Cox, D. R. and Snell, E. J.},170 title = {A General Definition of Residuals},171 journal = {Journal of the Royal Statistical Society: Series B},172 year = {1968}, volume = {30}, number = {2}, pages = {248--275},173 doi = {10.1111/j.2517-6161.1968.tb00724.x}174}175@article{cordeiro1991bias,176 author = {Cordeiro, Gauss M. and McCullagh, Peter},177 title = {Bias Correction in Generalized Linear Models},178 journal = {Journal of the Royal Statistical Society: Series B},179 year = {1991}, volume = {53}, number = {3}, pages = {629--643},180 doi = {10.1111/j.2517-6161.1991.tb01852.x}181}182@article{firth1993bias,183 author = {Firth, David},184 title = {Bias Reduction of Maximum Likelihood Estimates},185 journal = {Biometrika},186 year = {1993}, volume = {80}, number = {1}, pages = {27--38},187 doi = {10.1093/biomet/80.1.27}188}189@article{rilstone1996second,190 author = {Rilstone, Paul and Srivastava, V. K. and Ullah, Aman},191 title = {The Second-Order Bias and Mean Squared Error of Nonlinear Estimators},192 journal = {Journal of Econometrics},193 year = {1996}, volume = {75}, number = {2}, pages = {369--395},194 doi = {10.1016/0304-4076(96)89457-7}195}196@article{hahn2004jackknife,197 author = {Hahn, Jinyong and Newey, Whitney},198 title = {Jackknife and Analytical Bias Reduction for Nonlinear Panel Models},199 journal = {Econometrica},200 year = {2004}, volume = {72}, number = {4}, pages = {1295--1319},201 doi = {10.1111/j.1468-0262.2004.00533.x}202}203@incollection{lecam1956asymptotic,204 author = {Le Cam, Lucien},205 title = {On the Asymptotic Theory of Estimation and Testing Hypotheses},206 booktitle = {Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability},207 volume = {1}, pages = {129--156}, year = {1956},208 publisher = {University of California Press}, address = {Berkeley}209}210@article{bickel1975one,211 author = {Bickel, P. J.},212 title = {One-Step {H}uber Estimates in the Linear Model},213 journal = {Journal of the American Statistical Association},214 year = {1975}, volume = {70}, number = {350}, pages = {428--434},215 doi = {10.1080/01621459.1975.10479884}216}217@book{vandervaart1998asymptotic,218 author = {van der Vaart, A. W.},219 title = {Asymptotic Statistics},220 publisher = {Cambridge University Press}, address = {Cambridge}, year = {1998},221 doi = {10.1017/CBO9780511802256}222}223@article{bhattacharya1978validity,224 author = {Bhattacharya, R. N. and Ghosh, J. K.},225 title = {On the Validity of the Formal {E}dgeworth Expansion},226 journal = {Annals of Statistics},227 year = {1978}, volume = {6}, number = {2}, pages = {434--451},228 doi = {10.1214/aos/1176344134}229}230@article{duan2022heterogeneity,231 author = {Duan, Rui and Ning, Yang and Chen, Yong},232 title = {Heterogeneity-Aware and Communication-Efficient Distributed Statistical Inference},233 journal = {Biometrika},234 year = {2022}, volume = {109}, number = {1}, pages = {67--83},235 doi = {10.1093/biomet/asab007}236}237@article{szabo2020adaptive,238 author = {Szab{\'o}, Botond and van Zanten, Harry},239 title = {Adaptive Distributed Methods under Communication Constraints},240 journal = {Annals of Statistics},241 year = {2020}, volume = {48}, number = {4}, pages = {2347--2380},242 doi = {10.1214/19-AOS1890}243}244@article{cai2022distributed,245 author = {Cai, T. Tony and Wei, Hongji},246 title = {Distributed {G}aussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms},247 journal = {Annals of Statistics},248 year = {2022}, volume = {50}, number = {4}, pages = {1992--2020},249 doi = {10.1214/21-AOS2167}250}251@unpublished{duchi2014optimality,252 author = {Duchi, John C. and Jordan, Michael I. and Wainwright, Martin J. and Zhang, Yuchen},253 title = {Optimality Guarantees for Distributed Statistical Estimation},254 note = {arXiv:1405.0782},255 year = {2014}256}257@inproceedings{alimisis2021communication,258 author = {Alimisis, Foivos and Davies, Peter and Alistarh, Dan},259 title = {Communication-Efficient Distributed Optimization with Quantized Preconditioners},260 booktitle = {Proceedings of the 38th International Conference on Machine Learning},261 series = {Proceedings of Machine Learning Research}, volume = {139}, year = {2021}, publisher = {PMLR},262 note = {arXiv:2102.07214}263}264