Maria J. ESTEBAN Born at Alonsotegi
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Eurostat: Recognized Research Entity
http://ec.europa.eu/eurostat/web/microdata/overview This list enumerates entities that have been recognised as research entities by Eurostat. In order to apply for recognition please consult the document 'How to apply for microdata access?' http://ec.europa.eu/eurostat/web/microdata/overview The researchers of the entities listed below may submit research proposals. The research proposal will be assessed by Eurostat and the national statistical authorities which transmitted the confidential data concerned. Eurostat will regularly update this list and perform regular re-assessments of the research entities included in the list. Country City Research entity English name Research entity official name Member States BE Antwerpen University of Antwerp Universiteit Antwerpen Walloon Institute for Evaluation, Prospective Institut wallon pour l'Evaluation, la Prospective Belgrade and Statistics et la Statistique European Economic Studies Department, European Economic Studies Department, Bruges College of Europe College of Europe Brussels Applica sprl Applica sprl Brussels Bruegel Bruegel Center for Monitoring and Evaluation of Center for Monitoring and Evaluation of Brussels Research and Innovation, Belgian Science Research and Innovation, Service public Policy Office fédéral de Programmation Politique scientifique Centre for European Social and Economic Centre de politique sociale et économique Brussels Policy Asbl européenne Asbl Brussels Centre for European Policy Studies Centre for European Policy Studies Department for Applied Economics, -
ICN 2020 Committee
ICN 2020 Committee ICN Steering Committee Pascal Lorenz, University of Haute Alsace, France Yenumula B. Reddy, Grambling State University, USA Eric Renault, Institut Mines-Télécom - Télécom SudParis, France Sherali Zeadally, University of Kentucky, USA ICN Industry/Research Advisory Committee Marc Cheboldaeff, Deloitte Consulting GmbH, Germany Megumi Shibuya, The University of Electro-Communications, Japan Arslan Brömme, Vattenfall GmbH, Berlin, Germany Cristian Anghel, Politehnica University of Bucharest, Romania / Pentalog, France ICN 2020 Technical Program Committee Khelil Abdelmajid, Landshut University of Applied Sciences, Germany Alireza Abdollahpouri, University of Kurdistan, Sanandaj, Iran Abdelmuttlib Ibrahim Abdalla Ahmed, University of Malaya, Malaysia Ahmedin Mohammed Ahmed, FDRE Ministry of Innovation and Technology (MInT), Ethiopia Francisco Airton Silva, Federal University of Piauí, Brazil Sami Marzook Alesawi, King Abdulaziz University | Faculty of Computing and Information Technology at Rabigh, Saudi Arabia Madyan Alsenwi, Kyung Hee University - Global Campus, South Korea Reem Alshahrani, Kent State University, USA Cristian Anghel, Politehnica University of Bucharest, Romania / Pentalog, France Imran Shafique Ansari, University of Glasgow, Scotland, UK Suayb S. Arslan, MEF University, Turkey Mohammed A. Aseeri, King Abdulaziz City of Science and Technology (KACST), Kingdom of Saudi Arabia Michael Atighetchi, BBN Technologies, USA Jocelyn Aubert, Luxembourg Institute of Science and Technology (LIST), Luxembourg Marco Aurélio -
Iterated Importance Sampling in Missing Data Problems
Iterated importance sampling in missing data problems Gilles Celeux INRIA, FUTURS, Orsay, France Jean-Michel Marin ∗ INRIA, FUTURS, Orsay, France and CEREMADE, University Paris Dauphine, Paris, France Christian P. Robert CEREMADE, University Paris Dauphine and CREST, INSEE, Paris, France Abstract Missing variable models are typical benchmarks for new computational techniques in that the ill-posed nature of missing variable models offer a challenging testing ground for these techniques. This was the case for the EM algorithm and the Gibbs sampler, and this is also true for importance sampling schemes. A population Monte Carlo scheme taking advantage of the latent structure of the problem is proposed. The potential of this approach and its specifics in missing data problems are illustrated in settings of increasing difficulty, in comparison with existing approaches. The improvement brought by a general Rao–Blackwellisation technique is also discussed. Key words: Adaptive algorithms, Bayesian inference, latent variable models, population Monte Carlo, Rao–Blackwellisation, stochastic volatility model ∗ Corresponding author: CEREMADE, Place du Mar´echal De Lattre de Tassigny, 75775 Paris Cedex 16, France, [email protected] Preprint submitted to Computational Statistics and Data Analysis 25 July 2005 1 Introduction 1.1 Missing data models Missing data models, that is, structures such that the distribution of the data y can be represented via a marginal density Z f(y|θ) = g(y, z|θ)dz , Z where z ∈ Z denotes the so-called ”missing data”, have often been at the forefront of computational Statistics, both as a challenge to existing techniques and as a benchmark for incoming techniques. This is for instance the case with the EM algorithm (Dempster et al., 1977), which was purposely designed for missing data problems although it has since then been applied in a much wider setting. -
Masters Erasmus Mundus Coordonnés Par Ou Associant Un EESR Français
Les Masters conjoints « Erasmus Mundus » Masters conjoints « Erasmus Mundus » coordonnés par un établissement français ou associant au moins un établissement français Liste complète des Masters conjoints Erasmus Mundus : http://eacea.ec.europa.eu/erasmus_mundus/results_compendia/selected_projects_action_1_master_courses_en.php *Master n’offrant pas de bourses Erasmus Mundus *ACES - Joint Masters Degree in Aquaculture, Environment and Society (cursus en 2 ans) UK-University of the Highlands and Islands LBG FR- Université de Nantes GR- University of Crete http://www.sams.ac.uk/erasmus-master-aquaculture ADVANCES - MA Advanced Development in Social Work (cursus en 2 ans) UK-UNIVERSITY OF LINCOLN, United Kingdom DE-AALBORG UNIVERSITET - AALBORG UNIVERSITY FR-UNIVERSITÉ PARIS OUEST NANTERRE LA DÉFENSE PO-UNIWERSYTET WARSZAWSKI PT-UNIVERSIDADE TECNICA DE LISBOA www.socialworkadvances.org AMASE - Joint European Master Programme in Advanced Materials Science and Engineering (cursus en 2 ans) DE – Saarland University ES – Polytechnic University of Catalonia FR – Institut National Polytechnique de Lorraine SE – Lulea University of Technology http://www.amase-master.net ASC - Advanced Spectroscopy in Chemistry Master's Course FR – Université des Sciences et Technologies de Lille – Lille 1 DE - University Leipzig IT - Alma Mater Studiorum - University of Bologna PL - Jagiellonian University FI - University of Helsinki http://www.master-asc.org Août 2016 Page 1 ATOSIM - Atomic Scale Modelling of Physical, Chemical and Bio-molecular Systems (cursus -
Wtc Meeting Attendace Globecom 2017 Singapore
WTC MEETING ATTENDACE GLOBECOM 2017 SINGAPORE Najah Abu Ali University of the United Arab Emirates Ana García Armada University Carlos III de Madrid Ramy Amer Trinity College Dublin Alagan Anpalagan Ryerson University Imran Shafique Ansari TAMU at Qatar Nirwan Ansari NJIT Srikrishna Bhashyam IIT Madras Vijay K Bhargava University of British Columbia Suzhi Bi Shenzhen University Azzedine Boukerche University of Ottawa Wei Chen Tsinghua University Gaojie Chen University of Oxford Wen Chen Shanghai Jiao Tong University Shanzhi Chen Datang Zhiyong Chen Shanghai Jiao Tong University Xiaoming Chen Zhejiang University Bruno Clerckx Imperial College London Daniel Benevides da Costa Federal University of Ceará Luis M. Correia IST, University of Lisbon Shuguang Cui Texas A&M University/ UC Davis Huaiyu Dai North Carolina State University Linglong Dai Tsinghua University Swades De IIT Delhi Ugo Dias University of Brasilia Zhi Ding University of California, Davis Yinan Ding BUPT Zhiguo Ding Lancaster University Rui Dinis FCT - Universidade Nova de Lisboa, Portugal Octavia A. Dobre Memorial University Xiaojiang Du Temple University Salman Durrani Australian National University Hesham ElSawy King Abdullah University of Science and Technology Yuguang Fang University of Florida Mark Flanagan University College Dublin Hacène Fouchal University of Reims Yue Gao Queen Mary University of London 1 Ali Ghrayeb Texas A&M University at Qatar Andrea Giorgetti University of Bologna Chen Gong USTC Southern University of Science and Technology, Yi Gong China University of Idaho Mohsen Guizani Pengwenlong Gu Telecom ParisTech Li Guo BUPT Mounir Hamdi HBKU Shuai Han Harbin Institute of Technology Hossam Hassanein Queen's University Ruisi He Beijing Jiaotong University Bo Hu BUPT Jie Hu UESTC Rose Qingyang Hu Utah State University Cunqing Hua Shanghai Jiao Tong University Yunjian Jia Chongqing University Nan Jiang Queen Mary University of London Tamer Khattab Qatar University Chia-Han Lee Academia Sinica Khaled B. -
1 Appendix 6: Comparison of Year Abroad Partnerships with Our
Appendix 6: Comparison of year abroad partnerships with our national competitors Imperial College London’s current year abroad exchange links (data provided by Registry and reflects official exchange links for 2012-131) and their top 5 competitors’ (based on UCAS application data) exchange links are shown below. The data for competitors was confirmed either by a member of university staff (green) or obtained from their website (orange). Data was supplied/obtained between August and October 2012. Aeronautics Imperial College London France: École Centrale de Lyon, ENSICA – SupAero Germany: RWTH Aachen Singapore: National University of Singapore USA: University of California (Education Abroad Program) University of Cambridge France: École Centrale Paris Germany: Tech. University of Munich Singapore: National University of Singapore USA: Massachusetts Institute of Technology University of Oxford USA: Princeton University of Bristol Australia: University of Sydney Europe University of Southampton France: ESTACA, ENSICA – SupAero, DTUS – École Navale Brest Germany: University of Stuttgart Spain: Polytechnic University of Madrid Sweden: KTH University of Manchester Couldn’t find any evidence Bioengineering Imperial College London Australia: University of Melbourne France: Institut National Polytechnique de Grenoble Netherlands: TU Delft Singapore: National University of Singapore Switzerland: ETH Zurich USA: University of California (Education Abroad Program) University of Cambridge France: École Centrale Paris Germany: Tech. University of Munich -
SENSORCOMM 2018 Committee Page
SENSORCOMM 2018 Committee SENSORCOMM Steering Committee Sergey Yurish, IFSA, Spain Brendan O'Flynn, Tyndall National Institute | University College Cork, Ireland Jaime Lloret Mauri, Polytechnic University of Valencia, Spain Elad Schiller, Chalmers University of Technology, Sweden Jiannan Zhai, Florida Atlantic University, USA Stefano Mariani, Politecnico di Milano, Italy Manuela Vieira, ISEL-CTS/UNINOVA, Portugal Tadashi Okoshi, Keio University, Japan Jerker Delsing, Lulea University of Technology, Sweden SENSORCOMM Industry/Research Advisory Committee Shaohan Hu, IBM Research, USA SENSORCOMM 2018 Technical Program Committee Majid Bayani Abbasy, National University of Costa Rica, Costa Rica Rajat Aggarwal, DreamVu Inc., USA Amin Al-Habaibeh, Nottingham Trent University, UK Jesús B. Alonso Hernández, Institute for Technological Development and Innovation in Communications (IDeTIC) | University of Las Palmas de Gran Canaria (ULPGC), Spain Maykel Alonso Arce, CEIT and Tecnun (University of Navarra), Spain Mário Alves, Politécnico do Porto (ISEP/IPP), Portugal Andy Augousti, Kingston University, UK Paolo Bellavista, University of Bologna, Italy An Braeken, Vrije Universiteit Brussel, Belgium Erik Buchmann, Hochschule für Telekommunikation Leipzig, Germany Maria-Dolores Cano, Universidad Politécnica de Cartagena, Spain Juan-Vicente Capella-Hernández, Universitat Politècnica de València, Spain Vítor Carvalho, IPCA-EST-2Ai | Portugal & Algoritmi Research Centre, UM, Portugal Luca Caviglione, National Research Council of Italy (CNR), Italy Matteo Ceriotti, University of Duisburg-Essen, Germany Amitava Chatterjee, Jadavpur University, India Edmon Chehura, Cranfield University, UK Omar Cheikhrouhou, Taif University, Saudi Arabia Bill Chen, University of Macau, Macau Dixiang Chen, National University of Defense Technology, China Sungrae Cho, Chung-Ang University, South Korea Mario Cifrek, University of Zagreb, Croatia Victor Cionca, Cork Institute of Technology, Ireland Gautam K. -
AAAI-12 Conference Committees
AAAI 2012 Conference Committees Chairs and Cochairs AAAI Conference Committee Chair Dieter Fox (University of Washington, USA) AAAI12 Program Cochairs Jörg Hoffmann (Saarland University, Germany) Bart Selman (Cornell University, USA) IAAI12 Conference Chair and Cochair Markus Fromherz (ACS, a Xerox Company, USA) Hector Munoz‐Avila (Lehigh University, USA) EAAI12 Symposium Chair David Kauchak (Middlebury College, USA) Special Track on Artificial Intelligence and the Web Cochairs Denny Vrandecic (Institute of Applied Informatics and Formal Description Methods, Germany) Chris Welty (IBM Research, USA) Special Track on Cognitive Systems Cochairs Matthias Scheutz (Tufts University, USA) James Allen (University of Rochester, USA) Special Track on Computational Sustainability and Artificial Intelligence Cochairs Carla P. Gomes (Cornell University, USA) Brian C. Williams (Massachusetts Institute of Technology, USA) Special Track on Robotics Cochairs Kurt Konolige (Industrial Perception, Inc., USA) Siddhartha Srinivasa (Carnegie Mellon University, USA) Turing Centenary Events Chair Toby Walsh (NICTA and University of New South Wales, Australia) Tutorial Program Cochairs Carmel Domshlak (Technion Israel Institute of Technology, Israel) Patrick Pantel (Microsoft Research, USA) Workshop Program Cochairs Michael Beetz (University of Munich, Germany) Holger Hoos (University of British Columbia, Canada) Doctoral Consortium Cochairs Elizabeth Sklar (Brooklyn College, City University of New York, USA) Peter McBurney (King’s College London, United Kingdom) -
Testing Adverse Selection and Moral Hazard on French Car Insurance Data
TESTING ADVERSE SELECTION AND MORAL HAZARD ON FRENCH CAR INSURANCE DATA Guillaume CARLIER1 Université Paris Dauphine Michel GRUN-REHOMME2 Université Paris Panthéon Olga VASYECHKO3 Université Nationale d'Economie de Kyiv This paper is a modest contribution to the stream of research devoted to find empirical evidence of asymmetric information. Building upon Chiappori and Salanié's (2000) work, we propose two specific tests, one for adverse selection and one for moral hazard. We implement these tests on French car insurance data, circumventing the lack of dynamic data in our data base by a proxy of claim history. The first test suggests presence of adverse selection whereas the second one seems to contradict presence of pure moral hazard. Keywords: empirical evidence of moral hazard, adverse selection, car insurance. JEL Classification: C35, D82. 1 Université Paris Dauphine, CEREMADE, Pl. de Lattre de Tassigny, 75775 Paris Cedex 16, FRANCE, [email protected] 2 Université Paris Panthéon, ERMES, 12 Pl. du Panthéon, 75005 Paris, FRANCE, [email protected] 3 Université Nationale d'Economie de Kyiv, UKRAINE, [email protected] BULLETIN FRANÇAIS D’ACTUARIAT, Vol. 13, n° 25, janvier – juin 2013, pp. 117- 130 118 G. CARLIER – M. GRUN-REHOMME – O. VASYECHKO 1. INTRODUCTION There has been an intensive line of research in the last decades devoted to find empirical evidence of asymmetric information on insurance data. Under adverse selection, the insured has some private information about his type of risk, which the insurer cannot observe before the subscription of an insurance contract. The adverse selection assumption stipulates that the high-risks tend to choose more coverage than the low-risks (Rothschild and Stiglitz (1976), Wilson (1977), Spence (1978)). -
Dirk Hundertmark Department of Mathematics, MC-382 University of Illinois at Urbana-Champaign Altgeld Hall 1409 W
Dirk Hundertmark Department of Mathematics, MC-382 University of Illinois at Urbana-Champaign Altgeld Hall 1409 W. Green Street Urbana, IL 61801 +1 (217) 333-3350 (217) 333-9516 (fax) [email protected] 1401 W. Charles, Champaign, IL 61821 +1 (217) 419-1088 (home) http://www.math.uiuc.edu/∼dirk Personal Data German and American citizen, married, one daughter. Research interests Partial differential equations, analysis, variational methods, functional analysis, spectral theory, motivated by problems from Physics, especially quantum mechanics, and Engineering. Spectral theory of random operators and and its connection to statistical mechanics and some probabilistic problems from solid state physics. More recently, mathematical problems in non-linear fiber-optics, especially properties of dispersion managed solitons. Education 5/2003 Habilitation in Mathematics, Ludwig{Maximilians-Universit¨atM¨unchen. 7/1992 { 11/1996 Ph.D. (Dr. rer. nat., summa cum laude) in Mathematics, Ruhr- Universit¨atBochum, Germany. Thesis: \ On the theory of the magnetic Schr¨odingersemigroup." Advisor: Werner Kirsch 11/1985 { 2/1992 Study of Physics, Friedrich-Alexander-Universit¨atErlangen, Germany. Graduated with Diplom. Advisor: Hajo Leschke. Employment Since 9/2007 Member of the Institute of Condensed Matter Theory at UIUC. Since 8/2006 Associate Professor (tenured), Department of Mathematics, University of Illinois at Urbana-Champaign (on leave 8/2006 { 8/2007). 8/2006 {8/2007 Senior Lecturer, School of Mathematics, University of Birmingham, England. 1/2003 { 7/2006 Assistant Professor, Department of Mathematics, University of Illinois at Urbana-Champaign. 9 { 12/2002 Research fellow at the Institut Mittag-Leffler during the special program \Partial Differential Equations and Spectral Theory." 9/1999{8/2002 Olga Taussky-John Todd Instructor of Mathematics, Caltech. -
Mckean–Vlasov Optimal Control: the Dynamic Programming Principle
McKean–Vlasov optimal control: the dynamic programming principle∗ Mao Fabrice Djete† Dylan Possamaï ‡ Xiaolu Tan§ March 25, 2020 Abstract We study the McKean–Vlasov optimal control problem with common noise in various formulations, namely the strong and weak formulation, as well as the Markovian and non–Markovian formulations, and allowing for the law of the control process to appear in the state dynamics. By interpreting the controls as probability measures on an appropriate canonical space with two filtrations, we then develop the classical measurable selection, conditioning and concatenation arguments in this new context, and establish the dynamic programming principle under general conditions. 1 Introduction We propose in this paper to study the problem of optimal control of mean–field stochastic differential equations, also called Mckean–Vlasov stochastic differential equations in the literature. This problem is a stochastic control problem where the state process is governed by a stochastic differential equation (SDE for short), which has coefficients depending on the current time, the paths of the state process, but also its distribution (or conditional distribution in the case with common noise). Similarly, the reward functionals are allowed to be impacted by the distribution of the state process. The pioneering work on McKean–Vlasov equations is due to McKean [60] and Kac [49], who were interested in studying uncontrolled SDEs, and in establishing general propagation of chaos results. Let us also mention the illuminating notes of Snitzman [75], which give a precise and pedagogical insight into this specific equation. Though many authors have worked on this equation following these initial papers, there has been a drastic surge of interest in the topic in the past decade, due to the connection that it shares with the so–called mean–field game (MFG for short) theory, introduced independently and simultaneously on the one hand by Lasry and Lions in [53; 54; 55] and on the other hand by Huang, Caines, and Malhamé [44; 45; 46; 47; 48]. -
Time Averages for Kinetic Fokker-Planck Equations
Time averages for kinetic Fokker-Planck equations Giovanni M. Brigatia,b,1 aCEREMADE, CNRS, UMR 7534, Universit´eParis-Dauphine, PSL University, Place du Marechal de Lattre de Tassigny, 75016 Paris, France bDipartimento di Matematica “F. Casorati”, Universit`adegli Studi di Pavia, Via Ferrata 5, 27100 Pavia, Italia Abstract We consider kinetic Fokker-Planck (or Vlasov-Fokker-Planck) equations on the torus with Maxwellian or fat tail local equilibria. Results based on weak norms have recently been achieved by S. Armstrong and J.-C. Mourrat in case of Maxwellian local equilibria. Using adapted Poincar´eand Lions-type inequali- ties, we develop an explicit and constructive method for estimating the decay rate of time averages of norms of the solutions, which covers various regimes corresponding to subexponential, exponential and superexponential (including Maxwellian) local equilibria. As a consequence, we also derive hypocoercivity estimates, which are compared to similar results obtained by other techniques. Keywords: Kinetic Fokker-Planck equation, Ornstein-Uhlenbeck equation, time average, local equilibria, Lions’ lemma, Poincar´einequalities, hypocoercivity. 2020 MSC: Primary: 82C40; Secondary: 35B40, 35H10, 47D06, 35K65. 1. Introduction Let us consider the kinetic Fokker-Planck equation α−2 ∂tf + v ⋅ ∇xf ∇v ⋅ ∇vf + α v vf , f 0, ⋅, ⋅ f0. (1) = ⟨ ⟩ ( ) = where f is a function of time t 0, position x, velocity v, and α is a positive arXiv:2106.12801v1 [math.AP] 24 Jun 2021 parameter. Here we use the notation≥ d v »1 + v 2, ∀ v R . ⟨ ⟩ = S S ∈ d We consider the spatial domain Q ∶ 0,L x, with periodic boundary con- = ( ) ∋ ditions, and define Ωt ∶ t,t + τ × Q, for some τ 0, t 0 and Ω Ω0.