{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T06:58:45Z","timestamp":1758265125484},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2018,1,4]],"date-time":"2018-01-04T00:00:00Z","timestamp":1515024000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2018,9]]},"DOI":"10.1007\/s11432-016-9173-8","type":"journal-article","created":{"date-parts":[[2018,1,5]],"date-time":"2018-01-05T12:53:28Z","timestamp":1515156808000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Distributed regression estimation with incomplete data in multi-agent networks"],"prefix":"10.1007","volume":"61","author":[{"given":"Yinghui","family":"Wang","sequence":"first","affiliation":[]},{"given":"Peng","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Yiguang","family":"Hong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,1,4]]},"reference":[{"key":"9173_CR1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/TAC.2008.2009515","volume":"54","author":"A Nedi\u0107","year":"2009","unstructured":"Nedi\u0107 A, Ozdaglar A. Distributed subgradient methods for multi-agent optimization. IEEE Trans Automat Control, 2009, 54: 48\u201361","journal-title":"IEEE Trans Automat Control"},{"key":"9173_CR2","doi-asserted-by":"crossref","first-page":"3673","DOI":"10.1137\/110841308","volume":"51","author":"G Shi","year":"2013","unstructured":"Shi G, Johansson K. Robust consensus for continuous-time multiagent dynamics. SIAM J Control Optim, 2013, 51: 3673\u20133691","journal-title":"SIAM J Control Optim"},{"key":"9173_CR3","doi-asserted-by":"crossref","first-page":"3131","DOI":"10.1109\/TWC.2015.2402672","volume":"43","author":"Y Q Zhang","year":"2015","unstructured":"Zhang Y Q, Lou Y C, Hong Y G, et al. Distributed projection-based algorithms for source localization in wireless sensor networks. IEEE Trans Wirel Commun, 2015, 43: 3131\u20133142","journal-title":"IEEE Trans Wirel Commun"},{"key":"9173_CR4","first-page":"092103","volume":"57","author":"H Feng","year":"2014","unstructured":"Feng H, Jiang Z D, Hu B, et al. The incremental subgradient methods on distributed estimations in-network. Sci China Inf Sci, 2014, 57: 092103","journal-title":"Sci China Inf Sci"},{"key":"9173_CR5","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.automatica.2016.02.019","volume":"69","author":"Y C Lou","year":"2016","unstructured":"Lou Y C, Hong Y G, Wang S Y. Distributed continuous-time approximate projection protocols for shortest distance optimization problems. Automatica, 2016, 69: 289\u2013297","journal-title":"Automatica"},{"key":"9173_CR6","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.automatica.2016.08.007","volume":"74","author":"P Yi","year":"2016","unstructured":"Yi P, Hong Y G, Liu F. Initialization-free distributed algorithms for optimal resource allocation with feasibility con-straints and application to economic dispatch of power systems. Automatica, 2016, 74: 259\u2013269","journal-title":"Automatica"},{"key":"9173_CR7","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1109\/TIP.2004.823815","volume":"13","author":"A C Kokaram","year":"2004","unstructured":"Kokaram A C. On missing data treatment for degraded video and film archives: a survey and a new Bayesian approach. IEEE Trans Image Process, 2004, 13: 397\u2013415","journal-title":"IEEE Trans Image Process"},{"key":"9173_CR8","doi-asserted-by":"crossref","DOI":"10.1002\/9780470510445","volume-title":"Missing Data in Clinical Studies","author":"G Molenberghs","year":"2007","unstructured":"Molenberghs G, Kenward M G. Missing Data in Clinical Studies. New York: Wiley, 2007"},{"key":"9173_CR9","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1198\/016214504000001844","volume":"100","author":"J G Ibrahim","year":"2005","unstructured":"Ibrahim J G, Chen M H, Lipsitz S R, et al. Missing data methods for generalized linear models: a comparative review. J Am Stat Assoc, 2005, 100: 332\u2013346","journal-title":"J Am Stat Assoc"},{"key":"9173_CR10","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1109\/TSIPN.2016.2570679","volume":"2","author":"M R Gholami","year":"2016","unstructured":"Gholami M R, Jansson M, Strom E G, et al. Diffusion estimation over cooperative multi-agent networks with missing data. IEEE Trans Signal Inf Process Netw, 2016, 2: 276\u2013289","journal-title":"IEEE Trans Signal Inf Process Netw"},{"key":"9173_CR11","doi-asserted-by":"crossref","DOI":"10.4324\/9780203866955","volume-title":"Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach","author":"A Davey","year":"2009","unstructured":"Davey A, Savla J. Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach. Oxford, UK: Routledge Academic, 2009"},{"key":"9173_CR12","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1007\/s10957-010-9737-7","volume":"147","author":"S S Ram","year":"2010","unstructured":"Ram S S, Nedi\u0107 A, Veeravalli V V. Distributed stochastic subgradient projection algorithms for convex optimization. J Optim Theory Appl, 2010, 147: 516\u2013545","journal-title":"J Optim Theory Appl"},{"key":"9173_CR13","volume-title":"Regression Analysis: Concepts and Applications","author":"F Graybill","year":"1994","unstructured":"Graybill F, Iyer H K. Regression Analysis: Concepts and Applications. California: Duxbury Press Belmont, 1994"},{"key":"9173_CR14","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1109\/TKDE.2012.191","volume":"25","author":"Y Feng","year":"2013","unstructured":"Feng Y, Sundaram S, Vishwanathan S V N, et al. Distributed autonomous online learning: regrets and intrinsic privacy-preserving properties. IEEE Trans Knowl Data Eng, 2013, 25: 2483\u20132493","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9173_CR15","first-page":"2489","volume":"15","author":"E Hazan","year":"2014","unstructured":"Hazan E, Kale S. Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimiza-tion. J Mach Learn Res, 2014, 15: 2489\u20132512","journal-title":"J Mach Learn Res"},{"key":"9173_CR16","first-page":"71","volume-title":"Proceedings of International Conference on Machine Learning","author":"O Shamir","year":"2012","unstructured":"Shamir O, Zhang T. Stochastic gradient descent for non-smooth optimization: convergence results and optimal aver-aging schemes. In: Proceedings of International Conference on Machine Learning, Edinburgh, 2012. 71\u201379"},{"key":"9173_CR17","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.neucom.2012.12.043","volume":"112","author":"Z J Towfic","year":"2013","unstructured":"Towfic Z J, Chen J S, Sayed A H. On distributed online classification in the midst of concept drifts. Neurocomputing, 2013, 112: 138\u2013152","journal-title":"Neurocomputing"},{"key":"9173_CR18","first-page":"1","volume-title":"Adaptive Signal Processing","author":"B Widrow","year":"1985","unstructured":"Widrow B, Stearns S D. Adaptive Signal Processing. Cliffs: Prentice-Hall, 1985. 1\u201332"},{"key":"9173_CR19","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1561\/2200000051","volume":"7","author":"A H Sayed","year":"2014","unstructured":"Sayed A H. Adaptation, learning, and optimization over networks. Found Trends Mach Learn, 2014, 7: 311\u2013801","journal-title":"Found Trends Mach Learn"},{"key":"9173_CR20","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/MSP.2012.2231991","volume":"30","author":"A H Sayed","year":"2013","unstructured":"Sayed A H, Tu S Y, Chen J S, et al. Diffusion strategies for adaptation and learning. IEEE Signal Proc Mag, 2013, 30: 155\u2013171","journal-title":"IEEE Signal Proc Mag"},{"key":"9173_CR21","first-page":"2","volume-title":"Introduction to Optimization","author":"B T Polyak","year":"1983","unstructured":"Polyak B T. Introduction to Optimization. New York: Optimization Software Inc., 1983. 2\u20138"},{"key":"9173_CR22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-1-4613-0163-9","volume-title":"Algebraic Graph Theory","author":"C Godsil","year":"2001","unstructured":"Godsil C, Royle G. Algebraic Graph Theory. New York: Springer-Verlag, 2001. 1\u201318"},{"key":"9173_CR23","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-1-4899-4549-5_1","volume-title":"A Course in Large Sample Theory","author":"T S Ferguson","year":"1996","unstructured":"Ferguson T S. A Course in Large Sample Theory. London: Chapman and Hall Ltd., 1996. 3\u20134"},{"key":"9173_CR24","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1017\/CBO9780511779398.008","volume-title":"Probability Theory and Examples","author":"R Durrett","year":"2010","unstructured":"Durrett R. Probability Theory and Examples. Camberidge, UK: Camberidge Press, 2010. 328\u2013347"},{"key":"9173_CR25","volume-title":"Applied Missing Data Analysis","author":"C K Enders","year":"2010","unstructured":"Enders C K. Applied Missing Data Analysis. New York: The Guilford Press, 2010"},{"key":"9173_CR26","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/978-1-4899-2696-8","volume-title":"Stochastic Approximation and Recursive Algorithms and Applications","author":"H J Kushner","year":"1997","unstructured":"Kushner H J, Yin G. Stochastic Approximation and Recursive Algorithms and Applications. New York: Springer-Verlag, 1997. 117\u2013157"},{"key":"9173_CR27","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1109\/PROC.1976.10286","volume":"64","author":"B Widrow","year":"1976","unstructured":"Widrow B, Mccool J, Larimore M G, et al. Stationary and nonstationary learning characteristics of the LMS adaptive filter. Proc IEEE, 1976, 64: 1151\u20131162","journal-title":"Proc IEEE"},{"key":"9173_CR28","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s11768-015-5100-8","volume":"13","author":"P Yi","year":"2015","unstructured":"Yi P, Hong Y G. Stochastic sub-gradient algorithm for distributed optimization with random sleep scheme. Control Theory Technol, 2015, 13: 333\u2013347","journal-title":"Control Theory Technol"},{"key":"9173_CR29","first-page":"221","volume-title":"An Introduction to Mathematical Statistics and Its Applications","author":"R J Larsen","year":"2006","unstructured":"Larsen R J, Max M L. An Introduction to Mathematical Statistics and Its Applications. 4th ed. New York: Pearson, 2006. 221\u2013280"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-016-9173-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11432-016-9173-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-016-9173-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T02:08:25Z","timestamp":1570586905000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11432-016-9173-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,4]]},"references-count":29,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2018,9]]}},"alternative-id":["9173"],"URL":"https:\/\/doi.org\/10.1007\/s11432-016-9173-8","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,4]]},"article-number":"092202"}}