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Start Over Topic monte carlo method
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1. Bootstrap inference for Hawkes and general point processes

Author

U. Copenhagen Economics Discussion Paper Series, Giuseppe Cavaliere, Ye Lu, Anders Rahbek, and Jacob Østergaard

Subjects
Statistics::Theory, Computer science, Resampling, Monte Carlo method, Interval estimation, Nonparametric statistics, Statistics::Methodology, Estimator, Inference, Asymptotic theory (statistics), Algorithm, Point process
Abstract

Inference and testing in general point process models such as the Hawkes model is predominantly based on asymptotic approximations for likelihoodbased estimators and tests, as originally developed in Ogata (1978). As an alternative, and to improve finite sample performance, this paper considers bootstrap-based inference for interval estimation and testing. Specifically, for a wide class of point process models we consider a novel bootstrap scheme labeled 'fixed intensity bootstrap' (FIB), where the conditional intensity is kept fixed across bootstrap repetitions. The FIB, which is very simple to implement and fast in practice, naturally extends previous ideas from the bootstrap literature on time series in discrete time, where the so-called 'fixed design' and 'fixed volatility' bootstrap schemes have shown to be particularly useful and effective. We compare the FIB with the classic recursive bootstrap, which is here labeled 'recursive intensity bootstrap' (RIB). In RIB algorithms, the intensity is stochastic in the bootstrap world and implementation of the bootstrap is more involved, due to its sequential structure. For both bootstrap schemes, no asymptotic theory is available; we therefore provide here a new bootstrap (asymptotic) theory, which allows to assess bootstrap validity. We also introduce novel 'nonparametric' FIB and RIB schemes, which are based on resampling time-changed transformations of the original waiting times. We show effectiveness of the different bootstrap schemes in finite samples through a set of detailed Monte Carlo experiments. As far as we are aware, this is the first detailed Monte Carlo study of bootstrap implementations for Hawkes-type processes. Finally, in order to illustrate, we provide applications of the bootstrap to both financial data and social media data.

Published
2021

2. Monte Carlo Simulation and Density Functions

Author

David Paper

Subjects
Computer science, Monte Carlo method, Probability distribution, Statistical physics, Random variable, Randomness, Conjunction (grammar), Numerical integration
Abstract

Monte Carlo simulation (MCS) applies repeated random sampling (randomness) to obtain numerical results for deterministic problem solving. It is widely used in optimization, numerical integration, and risk-based decision making. Probability and cumulative density functions are statistical measures that apply probability distributions for random variables, and can be used in conjunction with MCS to solve deterministic problem.

Published
2018

5. Evolution Control for parallel ANN-assisted simulation-based optimization application to Tuberculosis Transmission Control

Author

Mohand-Said Mezmaz, Nouredine Melab, Romain Ragonnet, Guillaume Briffoteaux, Daniel Tuyttens, Optimisation de grande taille et calcul large échelle (BONUS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of Mons [Belgium] (UMONS), Monash University [Melbourne], Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), and Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).

Subjects
Artificial Neural Network, Computer Networks and Communications, Computer science, Monte Carlo method, Context (language use), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Simulation-based optimization, Surrogate-assisted Optimization, 0202 electrical engineering, electronic engineering, information engineering, Massively parallel, Dropout (neural networks), Artificial neural network, Evolution Control, business.industry, Deep learning, 020206 networking & telecommunications, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Supercomputer, Computer engineering, Hardware and Architecture, 020201 artificial intelligence & image processing, Artificial intelligence, Massively Parallel Computing, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, Software, Simulation
Abstract

International audience; In many optimal design searches, the function to optimise is a simulator that is computationally expensive. While current High Performance Computing (HPC) methods are not able to solve such problems efficiently, parallelism can be coupled with approximate models (surrogates or meta-models) that imitate the simulator in timely fashion to achieve better results. This combined approach reduces the number of simulations thanks to surrogate use whereas the remaining evaluations are handled by supercomputers. While the surrogates' ability to limit computational times is very attractive, integrating them into the over-arching optimization process can be challenging. Indeed, it is critical to address the major trade-off between the quality (precision) and the efficiency (execution time) of the resolution. In this article, we investigate Evolution Controls (ECs) which are strategies that define the alternation between the simulator and the surrogate within the optimization process. We propose a new EC based on the prediction uncertainty obtained from Monte Carlo Dropout (MCDropout), a technique originally dedicated to quantifying uncertainty in deep learning. Investigations of such uncertainty-aware ECs remain uncommon in surrogate-assisted evolutionary optimization. In addition, we use parallel computing in a complementary way to address the high computational burden. Our new strategy is implemented in the context of a pioneering application to Tuberculosis Transmission Control. The reported results show that the MCDropout-based EC coupled with massively parallel computing outperforms strategies previously proposed in the field of surrogate-assisted optimization.

Published
2020

6. On the Performance of Sliding Window TD-LMMSE Channel Estimation for 5G Waveforms in High Mobility Scenario

Author

Marion Berbineau, Iyad Dayoub, Michel Saideh, Département Composants et Systèmes (IFSTTAR/COSYS), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université de Lyon-PRES Université Nantes Angers Le Mans (UNAM)-PRES Université Lille Nord de France, Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France), Institut National des Sciences Appliquées (INSA), This work was supported in part by the SMARTIES project within theELSAT2020 program supported by FEDER, the French State, and the Haut deFrance Region and in part by the X2RAIL-1 project from H2020 SHIF2RAILprogram. The review of this paper was coordinated by Dr. N.-D. Dao., PRES Université Lille Nord de France-PRES Université Nantes Angers Le Mans (UNAM)-Université de Lyon-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), and Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)

Subjects
CHANNEL ESTIMATION, Computer Networks and Communications, Orthogonal frequency-division multiplexing, Computer science, Monte Carlo method, Aerospace Engineering, 02 engineering and technology, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], TRAITEMENT DU SIGNAL, TECHNOLOGIE SANS FIL, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], TELECOMMUNICATION, LMMSE, Sliding window protocol, 0202 electrical engineering, electronic engineering, information engineering, Waveform, [INFO]Computer Science [cs], Time domain, Electrical and Electronic Engineering, ONDE, Computer Science::Information Theory, 020208 electrical & electronic engineering, 020206 networking & telecommunications, FBMC, Filter bank, Computer Science::Other, [SPI.TRON]Engineering Sciences [physics]/Electronics, TRANSPORT FERROVIAIRE, Modulation, Automotive Engineering, SIMULATION, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 5G, Communication channel
Abstract

The upcoming 5G era is considered as a critical answer to future railway communication needs. In this correspondence, we analyze the performance of sliding window time domain (TD) LMMSE channel estimation technique where a generalized development is proposed and analyzed for 5G filter bank multicarrier-offset quadratic-amplitude modulation waveform candidates. In order to enhance performance, the analytical model is extended to exploit multipaths and multiantennas correlation wherever they do exist e.g., tunnels. TD-LMMSE performance for 5G waveforms and achieved enhancements are verified by Monte Carlo simulations.

Published
2018

7. Through the looking glass: Indirect inference via simple equilibria

Author

Veronika Czellar, Laurent E. Calvet, emlyon business school, Groupement de Recherche et d'Etudes en Gestion à HEC (GREGH), Ecole des Hautes Etudes Commerciales (HEC Paris)-Centre National de la Recherche Scientifique (CNRS), HEC Paris Research Paper Series, and business school, emlyon

Subjects
JEL: C - Mathematical and Quantitative Methods/C.C0 - General/C.C0.C01 - Econometrics, Large class, JEL: C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C53 - Forecasting and Prediction Methods • Simulation Methods, Economics and Econometrics, Hidden Markov model, long-run risk, learning, value at risk, indirect inference, particle filters, Computer science, Monte Carlo method, jel:C01, Indirect Inference, 01 natural sciences, Value at risk, Indirect inference, 010104 statistics & probability, Simple (abstract algebra), 0502 economics and business, Econometrics, JEL: C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C15 - Statistical Simulation Methods: General, Capital asset pricing model, Learning, Applied mathematics, JEL: C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C58 - Financial Econometrics, 050207 economics, 0101 mathematics, [SHS.ECO] Humanities and Social Sciences/Economics and Finance, Long-run risk, jel:C53, Applied Mathematics, 05 social sciences, JEL: C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C13 - Estimation: General, Estimator, jel:C13, [SHS.ECO]Humanities and Social Sciences/Economics and Finance, jel:C58, jel:C15, [SHS.GESTION]Humanities and Social Sciences/Business administration, Particle filters, [SHS.GESTION] Humanities and Social Sciences/Business administration, Particle filter
Abstract

International audience; This paper proposes an indirect inference (Gourieroux, Monfort and Renault, 1993; Smith, 1993) estimation method for a large class of dynamic equilibrium models. Our approach is based on the observation that the econometric structure of these systems naturally generates auxiliary equilibria that can serve as building blocks for estimation. We use this insight to develop an accurate estimator for the long-run risk model of Bansal and Yaron (2004). We demonstrate the accuracy of our method by Monte Carlo simulation and estimate the long-run risk model on U.S. data. We also illustrate the good performance of the methodology on an asset pricing model with investor learning.

Published
2015