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Journal of the American Statistical Association Volume 108, 2013 - Issue 501
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Applications and Case Studies

Estimating Latent Processes on a Network From Indirect Measurements

Edoardo M. Airoldi Department of Statistics, and Alfred P. Sloan Research Fellow, Harvard University, Cambridge, MA, 02138
&
Alexander W. Blocker Department of Statistics, Harvard University, Cambridge, MA, 02138
Pages 149-164 | Received 01 Sep 2011, Published online: 15 Mar 2013

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Wendy K. Tam Cho & George Judge. (2015) An information theoretic approach to network tomography. Applied Economics Letters 22:1, pages 1-6.
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Anastasia Mantziou, Simón Lunagómez & Robin Mitra. (2024) Bayesian model-based clustering for populations of network data. The Annals of Applied Statistics 18:1.
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Qian Wang, Guangbao Guo, Guoqi Qian & Xuejun Jiang. (2023) Distributed online expectation-maximization algorithm for Poisson mixture model. Applied Mathematical Modelling 124, pages 734-748.
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Marc SchnebleGöran Kauermann. (2020) Estimation of latent network flows in bike-sharing systems. Statistical Modelling 22:4, pages 349-378.
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Yichao Li & Ke Deng. (2022) Simultaneous Topology and Loss Tomography via a Modified Theme Dictionary Model. IEEE Transactions on Signal Processing 70, pages 4239-4251.
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M L Hazelton, M R Mcveagh & B van Brunt. (2021) Geometrically aware dynamic Markov bases for statistical linear inverse problems. Biometrika 108:3, pages 609-626.
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Jan Beran, Haiyan Liu & Sucharita Ghosh. (2019) On aggregation of strongly dependent time series. Scandinavian Journal of Statistics 47:3, pages 690-710.
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Anselmo Ramalho Pitombeira-Neto, Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho. (2020) A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks. Networks and Spatial Economics 20:2, pages 499-527.
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Y. Englezou, S. Timotheou & C.G. Panayiotou. (2019) Bayesian estimation of the Origin-Destination matrix using traffic flow dynamics. Bayesian estimation of the Origin-Destination matrix using traffic flow dynamics.
Susan Jia Xu, Mehdi Nourinejad, Xuebo Lai & Joseph Y. J. Chow. (2018) Network Learning via Multiagent Inverse Transportation Problems. Transportation Science 52:6, pages 1347-1364.
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Tao Wen, Chen Cai, Lauren Gardner, Steven Travis Waller, Vinayak Dixit & Fang Chen. (2018) Estimation of sparse O–D matrix accounting for demand volatility. IET Intelligent Transport Systems 12:9, pages 1020-1026.
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Tao Wen, Lauren Gardner, Vinayak Dixit, S. Travis Waller, Chen Cai & Fang Chen. (2017) Two Methods to Calibrate the Total Travel Demand and Variability for a Regional Traffic Network. Computer-Aided Civil and Infrastructure Engineering 33:4, pages 282-299.
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Martin L. Hazelton. 2014. Wiley StatsRef: Statistics Reference Online. Wiley StatsRef: Statistics Reference Online 1 6 .
Martin L. Hazelton & Timothy P. Bilton. (2017) Polytope samplers for network tomography. Australian & New Zealand Journal of Statistics 59:4, pages 495-511.
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Ugo Fiore, Paolo Zanetti, Francesco Palmieri & Francesca Perla. (2017) Traffic matrix estimation with software-defined NFV: Challenges and opportunities. Journal of Computational Science 22, pages 162-170.
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Martin L. Hazelton & Katharina Parry. (2016) Statistical methods for comparison of day-to-day traffic models. Transportation Research Part B: Methodological 92, pages 22-34.
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Ke Deng, Yang Li, Weiping Zhu & Jun S. Liu. (2016) Fast parameter estimation in loss tomography for networks of general topology. The Annals of Applied Statistics 10:1.
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Ricardo SilvaSoong Moon Kang & Edoardo M. Airoldi. (2015) Predicting traffic volumes and estimating the effects of shocks in massive transportation systems. Proceedings of the National Academy of Sciences 112:18, pages 5643-5648.
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Martin L. Hazelton. (2015) Network tomography for integer-valued traffic. The Annals of Applied Statistics 9:1.
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Denis Pedyash, Chunsheng Shi, Varvara Nosova & Alexey V. Belov. (2014) Influence of CEO’s Nationality on Organizational Structure and Design, on Corporative Culture and on Innovation (In Case of Canadian and Russian Enterprises Based in China). Journal of Applied Sciences 14:9, pages 944-949.
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