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Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM

Wolfgang Jank
2005 Computational Statistics & Data Analysis  
In this paper we investigate an efficient implementation of the Monte Carlo EM algorithm based on Quasi-Monte Carlo sampling.  ...  Quasi-Monte Carlo methods produce deterministic sequences of points that can significantly improve the accuracy of Monte Carlo approximations over purely random sampling.  ...  Acknowledgements All the simulations made in this work are based on the programming language Ox of Doornik (2001) .  ... 
doi:10.1016/j.csda.2004.03.019 fatcat:rsnpej2noregzljw4xp5fnhh4q

Quasi-Monte Carlo in finance: extending for problems of high effective dimension

Marcos Eugênio da Silva, Thierry Barbe
2005 Economia Aplicada  
ABSTRACT In this paper we show that it is possible to extend the use of quasi-Monte Carlo for applications of high effective dimension.  ...  RESUMO Neste artigo mostramos que é possível usar métodos de simulação quase-Monte Carlo em problemas de alta dimensão efetiva.  ...  Again, quasi-Monte Carlo with the Schur decomposition improves immensely on crude Monte Carlo.  ... 
doi:10.1590/s1413-80502005000400004 fatcat:qij65qnberchnm2ruxhvtzpawa

Cross-section measurement for quasi-elastic production of charmed baryons in νN interactions

A. Kayis-Topaksu, G. Onengüt, R. van Dantzig, M. de Jong, O. Melzer, R.G.C. Oldeman, E. Pesen, F.R. Spada, J.L. Visschers, M. Güler, U. Köse, M. Serin-Zeyrek (+97 others)
2003 Physics Letters B  
At the average neutrino energy of 27 GeV the cross-section for the total quasi-elastic production of charmed baryons relative to the νN charged-current cross-section was measured to be σ (QE)/σ (CC) =  ...  A final sample containing 13 candidates consistent with quasi-elastic production of a charmed baryon with an estimated background of 1.7 events was obtained.  ...  Acknowledgements We gratefully acknowledge the help and support of the neutrino beam staff and of the numerous technical collaborators who contributed to the detector construction, operation, emulsion  ... 
doi:10.1016/j.physletb.2003.09.056 fatcat:bvalecy6szawrh2nqr67d7llce

Nesting Monte Carlo EM for high-dimensional item factor analysis

Xinming An, Peter M. Bentler
2013 Journal of Statistical Computation and Simulation  
Keywords full information item factor analysis; Monte Carlo EM; nesting EM  ...  Simulation studies and a real data example suggest that the Nesting MCEM approach can significantly improve computational efficiency while also enjoying the good properties of stable convergence and easy  ...  (2) Monte Carlo based EM (MCEM) [14] ; (3) Monte Carlo based stochastic approximation [15] .  ... 
doi:10.1080/00949655.2011.599810 pmid:23329857 pmcid:PMC3544932 fatcat:x446zqruzvhzjmeag32dik6l6i

Polynomial Chaos-Based Approach to Yield-Driven EM Optimization

Jianan Zhang, Chao Zhang, Feng Feng, Wei Zhang, Jianguo Ma, Qi-Jun Zhang
2018 IEEE transactions on microwave theory and techniques  
The proposed objective function requires fewer EM simulations to provide reliable yield representation than that in the conventional Monte Carlo-based yield optimization approach.  ...  For the first time, we extend in this paper the use of polynomial chaos (PC) approach from electromagnetic (EM)-based yield estimation to EM-based yield optimization of microwave structures.  ...  The PC coefficients are able to provide the statistical properties and approximate the PDF of the EM response with fewer EM samples than that required by the Monte Carlo analysis.  ... 
doi:10.1109/tmtt.2018.2834526 fatcat:37fyyjhs7fgxtgalddbpif5mgy

Monte Carlo Methods in Statistics [article]

Christian P. Robert
2009 arXiv   pre-print
Monte Carlo methods are now an essential part of the statistician's toolbox, to the point of being more familiar to graduate students than the measure theoretic notions upon which they are based!  ...  We recall in this note some of the advances made in the design of Monte Carlo techniques towards their use in Statistics, referring to Robert and Casella (2004,2010) for an in-depth coverage.  ...  The basic Monte Carlo principle and its extensions The most appealing feature of Monte Carlo methods [for a statistician] is that they rely on sampling and on probability notions, which are the bread and  ... 
arXiv:0909.0389v1 fatcat:xihjgo3pdvattd2iw4uyzlv7ny

Conditional Logistic Regression With Longitudinal Follow-up and Individual-Level Random Coefficients: A Stable and Efficient Two-Step Estimation Method

Radu V. Craiu, Thierry Duchesne, Daniel Fortin, Sophie Baillargeon
2011 Journal of Computational And Graphical Statistics  
The second step uses the EM-algorithm in conjunction with conditional restricted maximum likelihood to estimate the population parameters.  ...  In this case, the denominator of each cluster contribution to the conditional likelihood involves a complex integral in high dimension, which leads to convergence problems in the numerical maximization  ...  ACKNOWLEDGMENTS We thank the editor, an associate editor, and two referees for a set of thorough reviews that have greatly improved the article.  ... 
doi:10.1198/jcgs.2011.09189 fatcat:meryrjwxmnfzfmfq5hd2xhjrku

Monte Carlo Methods in Statistics [chapter]

Christian Robert
2011 International Encyclopedia of Statistical Science  
The basic Monte Carlo principle and its extensions The most appealing feature of Monte Carlo methods [for a statistician] is that they rely on sampling and on probability notions, which are the bread and  ...  distributions (and hence the potential of implementing the Gibbs sampler) was far from negligible, especially when the availability of latent variables became quasi universal due to the slice sampling  ... 
doi:10.1007/978-3-642-04898-2_376 fatcat:zi52is65jfe6tiij6h73mhawpa

Maximum Likelihood Algorithms for Generalized Linear Mixed Models

Charles E. McCulloch
1997 Journal of the American Statistical Association  
We show how to construct a Monte Carlo version of the EM algorithm, propose a Monte Carlo Newton·Raphson algorithm and evaluate and improve the use of importance sampling ideas.  ...  We also use the Newton-Raphson algorithm as a framework to compare maximum likelihood with the ')ointmaximization" or penalized quasi-likelihood methods and explain why the latter can perform poorly.  ...  JM is the joint-maximization (or penalized quasi-likelihood) method, MCEM is Monte Carlo EM, MCNR is Monte Carlo Newton-Raphson.  ... 
doi:10.1080/01621459.1997.10473613 fatcat:fysvw2or3fhzjkep7sqwpghah4

Measurement of Z/ $\gamma^*$ production in compton scattering of quasi-real photons

G. Abbiendi et al.
2002 European Physical Journal C: Particles and Fields  
The differential cross-sections of the Mandelstam variables s-hat, t-hat, and u-hat are measured and compared with the predictions from the Monte Carlo generators grc4f and PYTHIA.  ...  The cross-section times the branching ratio of the Z/gamma* decaying into hadrons is measured within Lorentz invariant kinematic limits to be (1.2 +/- 0.3 +/- 0.1) pb for invariant masses of the hadronic  ...  We particularly wish to thank the SL Division for the efficient operation of the LEP accelerator at all energies and for their continuing close cooperation with our experimental group.  ... 
doi:10.1007/s100520100875 fatcat:bhag2r67sjemtizwgvnokgioee

Subject Index [chapter]

2007 Advances in Chemical Physics  
221-222 Efficient microcanonical sampling (EMS), microcanonical approach, 245-249 diagrams, smoothing of, 307-308 protein folding, 206-207 Effective potentials, macrostate trajectory principles and equations  ...  , 101-104 diffusion Monte Carlo, 97-111 structure prediction, 228 importance sampling, 99-101 improved algorithm, 105-1 11 Ergodic systems: limits of, 104-105 simple algorithm, 97-105 Metropolis Monte  ... 
doi:10.1002/9780470141649.indsub fatcat:uwedjpuxzzcn3hiphjzgbdr65e

Monte Carlo approximation through Gibbs output in generalized linear mixed models

Jennifer S.K. Chan, Anthony Y.C. Kuk, Carrie H.K. Yam
2005 Journal of Multivariate Analysis  
We attempt to improve the method by assigning some prior information to the parameters and using the Gibbs output to evaluate the marginal likelihood and its derivatives through a Monte Carlo approximation  ...  Soc. 56 (1994) 291) proposed Monte Carlo method to approximate the whole likelihood function. His method is limited to choosing a proper reference point.  ...  Acknowledgements The authors are grateful to the editor and referees for their valuable comments and helpful suggestions which led to improvements in the paper. The research of J.S.K.  ... 
doi:10.1016/j.jmva.2004.05.004 fatcat:tpzic6y5qjecfm5fkapzct3uam

Page 4457 of Mathematical Reviews Vol. , Issue 87h [page]

1987 Mathematical Reviews  
Molchanov (Kiev) Niederreiter, Harald (A-OAW); Peart, Paul (JA-UWI) Localization of search in quasi-Monte Carlo methods for global optimization. SIAM J. Sct. Statist.  ...  Transport of higher-order derivatives can be used to improve the smoothness.  ... 

Hierarchical Decompositions for the Computation of High-Dimensional Multivariate Normal Probabilities

Marc G. Genton, David E. Keyes, George Turkiyyah
2018 Journal of Computational And Graphical Statistics  
It allows the reduction of the computational complexity per Monte Carlo sample from O(n 2 ) to O(mn + knlog(n/m)), where k is the numerical rank of off-diagonal matrix blocks and m is the size of small  ...  This hierarchical decomposition leads to substantial efficiencies in multivariate normal probability computations and allows integrations in thousands of dimensions to be practical on modern workstations  ...  A more critical limitation though is the O(n 2 ) cost per Monte Carlo sample leading to an overall complexity of O(N n 2 ), where N is the number of (quasi-) Monte Carlo samples used in the evaluation.  ... 
doi:10.1080/10618600.2017.1375936 fatcat:rj5apti36fbozenaa7zvkdizuy

Properties of Monte Carlo and Its Application to Risk Management

Rui-mei Li
2015 International Journal of u- and e- Service, Science and Technology  
This paper focuses on the application of Monte Carlo method in project management as follow: first, deals with the estimated scale with Wide band Delphi method and gets the interval of prediction after  ...  making repeated samples in line with the generally adopted probability models; then figures out the workloads according to the management abilities and historical date; finally obtains the progress of  ...  Introduction of Monte Carlo Method Monte Carlo method [9] [10] [11] [12] is a highly efficient calculation method with the basic idea being treating the frequency of an event approximately as its probability  ... 
doi:10.14257/ijunesst.2015.8.9.37 fatcat:fi23a5a6ofahpamfyas4ohmbb4
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