Fetching the paper…
Reading the bibliography…
The EM algorithm is a powerful tool for maximum likelihood estimation with missing data.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
Earlier work this paper cites.
Stochastic estimation of the maximum of a regression function
J. Kiefer and J. Wolfowitz · 1952
Earlier work this paper cites.
The relationship of cancer of the lung and the use of tobacco
Harold F. Dorn · 1954
Earlier work this paper cites.
On stochastic approximation
Aryeh Dvoretzky · 1956
Earlier work this paper cites.
Maximum likelihood from incomplete data via the EM algorithm
A. P. Dempster, N. M. Laird, and D. B. Rubin · 1977
Earlier work this paper cites.
The distribution of the ABO blood groups in Japan
Yoshiko Fujita, Masako Tanimura, and Katumi Tanaka · 1978
Earlier work this paper cites.
Finding the observed information matrix when using the EM algorithm
Thomas A. Louis · 1982
Earlier work this paper cites.
On the convergence properties of the EM algorithm
C. F. Jeff Wu · 1983
Earlier work this paper cites.
The SEM algorithm: a probabilistic teacher algorithm derived from the EM algorithm for the mixture problem
G. Celeux and J. Diebolt · 1985
Earlier work this paper cites.
The EM and SEM algorithms for mixtures: statistical and numerical aspects
Gilles Celeux and Jean Diebolt · 1987
Earlier work this paper cites.
Generalized Linear Models
P. McCullagh and J. A. Nelder · 1989
Earlier work this paper cites.
A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms
Greg C. G. Wei and Martin A. Tanner · 1990
Earlier work this paper cites.
Markov chain Monte Carlo maximum likelihood
Charles J. Geyer · 1991
Earlier work this paper cites.
A stochastic approximation type EM algorithm for the mixture problem
Gilles Celeux and Jean Diebolt · 1992
Earlier work this paper cites.
Acceleration of stochastic approximation by averaging
B. T. Polyak and A. B. Juditsky · 1992
Earlier work this paper cites.
Maximum-likelihood estimation for constrained- or missing-data models
Alan E. Gelfand and Bradley P. Carlin · 1993
Earlier work this paper cites.
Maximum likelihood estimation via the ECM algorithm: A general framework
Xiao-Li Meng and Donald B. Rubin · 1993
Earlier work this paper cites.
Tools for Statistical Inference
Martin A. Tanner · 1993
Earlier work this paper cites.
On the convergence of Monte Carlo maximum likelihood calculations
Charles J. Geyer · 1994
Earlier work this paper cites.
On the global and componentwise rates of convergence of the EM algorithm
Xiao-Li Meng and Donald B. Rubin · 1994
Earlier work this paper cites.
On stochastic versions of the EM algorithm
Gilles Celeux, Didier Chauveau, and Jean Diebolt · 1995
Earlier work this paper cites.
Monte Carlo EM estimation for time series models involving counts
K. S. Chan and Johannes Ledolter · 1995
Earlier work this paper cites.
Tools for Statistical Inference
Martin A. Tanner · 1996
Earlier work this paper cites.
The Monte Carlo Newton-Raphson algorithm
Anthony Y. C. Kuk and Yuk W. Cheng · 1997
Earlier work this paper cites.
Stochastic Approximation Algorithms and Applications
Harold J. Kushner and G. George Yin · 1997
Earlier work this paper cites.
Maximum likelihood algorithms for generalized linear mixed models
Charles E. McCulloch · 1997
Earlier work this paper cites.
Monte Carlo and quasi-Monte Carlo methods
Russel E. Caflisch · 1998
Earlier work this paper cites.
A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems
Ming Gao Gu and Fan Hui Kong · 1998
Earlier work this paper cites.
A stochastic approximation algorithm for maximum-likelihood estimation with incomplete data
Ming Gao Gu and Shaolin Li · 1998
Cited alongside, same era.
Asymptotic Statistics
A. W. van der Vaart · 1998
Cited alongside, same era.
Maximizing generalized linear mixed model likelihoods with an automated monte carlo em algorithm
James G. Booth and James P. Hobert · 1999
Cited alongside, same era.
Convergence of a stochastic approximation version of the EM algorithm
Bernard Delyon, Marc Lavielle, and Eric Moulines · 1999
Cited alongside, same era.
Direct calculation of the information matrix via the EM algorithm
David Oakes · 1999
Cited alongside, same era.
Monte Carlo EM with importance reweighting and its applications in random effects models
Fernando A. Quintana, Jun S. Liu, and Guido E. del Pino · 1999
Quasi-Monte Carlo for highly structured generalized response models
F. Y. Kuo, W. T. M. Dunsmuir, I. H. Sloan, M. P. Wand, and R. S. Womersley · 2008
Later among the works it cites.
The EM algorithm and extensions
Geoffrey J. McLachlan and Thriyambakam Krishnan · 2008
Later among the works it cites.
The variational approximation for Bayesian inference
Dimitris G. Tsikas, Aristidis C. Likas, and Nikolaos P. Galatsanos · 2008
Later among the works it cites.
The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2009
Later among the works it cites.
Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
Later among the works it cites.
High-dimensional exploratory item factor analysis by a Metropolis-Hastings Robbins-Monro algorithm
Li Cai · 2010
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model
James G. Booth, James P. Hobert, and Wolfgang Jank · 2001
Cited alongside, same era.
Maximum likelihood estimation for spatial models by Markov Chain Monte Carlo stochastic approximation
Ming Gao Gu and Hong-Tu Zhu · 2001
Cited alongside, same era.
Implementations of the Monte Carlo EM algorithm
Richard A. Levine and George Casella · 2001
Cited alongside, same era.
Recent advances in randomized quasi-Monte Carlo methods
Pierre L’Ecuyer and Christiane Lemieux · 2002
Cited alongside, same era.
Maximum likelihood inference for multivariate frailty models using an automated Monte Carlo EM algorithm
Samuli Ripatti, Klaus Larsen, and Juni Palmgren · 2002
Cited alongside, same era.
Convergence of the Monte Carlo expectation maximization for curved exponential families
Gersende Fort and Eric Moulines · 2003
Cited alongside, same era.
Bayesian Data Analysis
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin · 2013
Later among the works it cites.
On convergence properties of the Monte Carlo EM algorithm
Ronald C. Neath · 2013
Later among the works it cites.
A sequential Monte Carlo approach for the MLE in a plant growth model
Trevezas and Cournède · 2013
Later among the works it cites.
Sequential Monte Carlo EM for multivariate probit models
Giusi Moffa and Jack Kuipers · 2014
Later among the works it cites.
Parameter estimation via stochastic variants of the ecm algorithm with applications to plant growth modelling
S. Trevezas, S. Malefaki, and P.-H. Cournède · 2014
Later among the works it cites.
The top 100 papers
Richard Van Noorden, Brendan Maher, and Regina Nuzzo · 2014
Later among the works it cites.
A non linear mixed effects model of plant growth and estimation via stochastic variants of the EM algorithm
Baey, Trevezas, and Cournède · 2016
Later among the works it cites.
Using motorettes for the experimental and numerical determinations of the pdiv in an electric motor
P. Rain, F. Loubeau, A. Durieux, F. Le Strat, and F. Fresnet · 2016
Later among the works it cites.
Importance sampling: Intrinsic dimension and computational cost
S. Agapiou, O. Papaspiliopoulos, D. Sanz-Alonso, and A. M. Stuart · 2017
Later among the works it cites.
Julia: a fresh approach to numerical computing
Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Vial B. Shah · 2017
Later among the works it cites.
Variational inference: a review for statisticians
David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe · 2017
Later among the works it cites.
Adaptive importance sampling: The past, the present, and the future
Mónica F. Bugallo, Víctor Elvira, Luca Martino, David Luengo, Joaquín Míguez, and Petar M. Djurić · 2017
Later among the works it cites.
Generalized multiple importance sampling
Víctor Elvira, Luca Martino, David Luengo, and Mónica Bugallo · 2019
Later among the works it cites.
An Introduction to Sequential Monte Carlo
Nicolas Chopin and Omiros Papaspiliopoulos · 2020
Later among the works it cites.
Challenges and strategies in analysis of missing data
Xiao-Hua Zhou · 2020
Later among the works it cites.
Stochastic Approximation: A dynamical systems viewpoint
Vivek S. Borkar · 2022
Later among the works it cites.
Advances in importance sampling
Víctor Elvira and Luca Martino · 2022
Later among the works it cites.
Stan modelling language users guide and reference manual, 2022
Stan Development Team · 2022
Later among the works it cites.
Pareto smoothed importance sampling, 2022
Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, and Jonah Gabry · 2022
Later among the works it cites.
MCEM_Survey, 2023
William Ruth and Richard Lockhart · 2023
Later among the works it cites.
RStan: the R interface to Stan, 2023
Stan Development Team · 2023
Later among the works it cites.