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Maximum likelihood estimation in nonlinear mixed effects models

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Cited by:

  1. Allassonnière, Stéphanie & Chevallier, Juliette, 2021. "A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
  2. Charlotte Baey & Amélie Mathieu & Alexandra Jullien & Samis Trevezas & Paul-Henry Cournède, 2018. "Mixed-Effects Estimation in Dynamic Models of Plant Growth for the Assessment of Inter-individual Variability," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 208-232, June.
  3. Sodjahin, Ibirénoyé Honoré Romaric & Féménia, Fabienne & Koutchade, Obafémi Philippe & Carpentier, Alain, 2022. "On the economic value of the agronomic effects of crop diversification for farmers: estimation based on farm cost accounting data," Working Papers 320398, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
  4. Hanwen Huang, 2022. "Bayesian multi‐level mixed‐effects model for influenza dynamics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1978-1995, November.
  5. Solène Desmée & France Mentré & Christine Veyrat-Follet & Bernard Sébastien & Jérémie Guedj, 2017. "Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients," Biometrics, The International Biometric Society, vol. 73(1), pages 305-312, March.
  6. Munch, Jakob R. & Nguyen, Daniel X., 2014. "Decomposing firm-level sales variation," Journal of Economic Behavior & Organization, Elsevier, vol. 106(C), pages 317-334.
  7. Delattre, M. & Lavielle, M., 2012. "Maximum likelihood estimation in discrete mixed hidden Markov models using the SAEM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2073-2085.
  8. Umberto Picchini & Andrea De Gaetano & Susanne Ditlevsen, 2010. "Stochastic Differential Mixed‐Effects Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 67-90, March.
  9. Sodjahin, Romaric & Carpentier, Alain & Koutchade, Obafèmi Philippe & Femenia, Fabienne, 2022. "On the economic value of the agronomic effects of crop diversification for farmers: Estimation based on farm cost accounting data," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322295, Agricultural and Applied Economics Association.
  10. Larissa A. Matos & Víctor H. Lachos & Tsung-I Lin & Luis M. Castro, 2019. "Heavy-tailed longitudinal regression models for censored data: a robust parametric approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 844-878, September.
  11. Ollier, Edouard & Samson, Adeline & Delavenne, Xavier & Viallon, Vivian, 2016. "A SAEM algorithm for fused lasso penalized NonLinear Mixed Effect Models: Application to group comparison in pharmacokinetics," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 207-221.
  12. Fu, Eric & Heckman, Nancy, 2019. "Model-based curve registration via stochastic approximation EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 159-175.
  13. Wang, Jing, 2007. "EM algorithms for nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3244-3256, March.
  14. Baey, Charlotte & Cournède, Paul-Henry & Kuhn, Estelle, 2019. "Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 107-122.
  15. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
  16. María José Lombardía & Stefan Sperlich, 2008. "Semiparametric inference in generalized mixed effects models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 913-930, November.
  17. Nguyen, Thu Thuy & Mentré, France, 2014. "Evaluation of the Fisher information matrix in nonlinear mixed effect models using adaptive Gaussian quadrature," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 57-69.
  18. Umberto Picchini & Adeline Samson, 2018. "Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models," Computational Statistics, Springer, vol. 33(1), pages 179-212, March.
  19. Artémis Llamosi & Andres M Gonzalez-Vargas & Cristian Versari & Eugenio Cinquemani & Giancarlo Ferrari-Trecate & Pascal Hersen & Gregory Batt, 2016. "What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-18, February.
  20. Christian E. Galarza & Luis M. Castro & Francisco Louzada & Victor H. Lachos, 2020. "Quantile regression for nonlinear mixed effects models: a likelihood based perspective," Statistical Papers, Springer, vol. 61(3), pages 1281-1307, June.
  21. Baey, Charlotte & Didier, Anne & Lemaire, Sébastien & Maupas, Fabienne & Cournède, Paul-Henry, 2013. "Modelling the interindividual variability of organogenesis in sugar beet populations using a hierarchical segmented model," Ecological Modelling, Elsevier, vol. 263(C), pages 56-63.
  22. Laura Azzimonti & Francesca Ieva & Anna Maria Paganoni, 2013. "Nonlinear nonparametric mixed-effects models for unsupervised classification," Computational Statistics, Springer, vol. 28(4), pages 1549-1570, August.
  23. Yong Dam Jeong & William S. Hart & Robin N. Thompson & Masahiro Ishikane & Takara Nishiyama & Hyeongki Park & Noriko Iwamoto & Ayana Sakurai & Michiyo Suzuki & Kazuyuki Aihara & Koichi Watashi & Eline, 2024. "Modelling the effectiveness of an isolation strategy for managing mpox outbreaks with variable infectiousness profiles," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  24. Celine Marielle Laffont & Marc Vandemeulebroecke & Didier Concordet, 2014. "Multivariate Analysis of Longitudinal Ordinal Data With Mixed Effects Models, With Application to Clinical Outcomes in Osteoarthritis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 955-966, September.
  25. Elson Tomás & Susana Vinga & Alexandra M. Carvalho, 2017. "Unsupervised learning of pharmacokinetic responses," Computational Statistics, Springer, vol. 32(2), pages 409-428, June.
  26. Kim, Seong-Joon & Mun, Byeong Min & Bae, Suk Joo, 2019. "A cost-driven reliability demonstration plan based on accelerated degradation tests," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 226-239.
  27. Sy-Miin Chow & Zhaohua Lu & Andrew Sherwood & Hongtu Zhu, 2016. "Fitting Nonlinear Ordinary Differential Equation Models with Random Effects and Unknown Initial Conditions Using the Stochastic Approximation Expectation–Maximization (SAEM) Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 81(1), pages 102-134, March.
  28. Kim, Seong-Joon & Bae, Suk Joo, 2013. "Cost-effective degradation test plan for a nonlinear random-coefficients model," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 68-79.
  29. Wang, Xiaoning & Schumitzky, Alan & D'Argenio, David Z., 2007. "Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6614-6623, August.
  30. repec:dau:papers:123456789/11429 is not listed on IDEAS
  31. Daniel B. Reeves & Christian Gaebler & Thiago Y. Oliveira & Michael J. Peluso & Joshua T. Schiffer & Lillian B. Cohn & Steven G. Deeks & Michel C. Nussenzweig, 2023. "Impact of misclassified defective proviruses on HIV reservoir measurements," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  32. Daniel B. Reeves & Charline Bacchus-Souffan & Mark Fitch & Mohamed Abdel-Mohsen & Rebecca Hoh & Haelee Ahn & Mars Stone & Frederick Hecht & Jeffrey Martin & Steven G. Deeks & Marc K. Hellerstein & Jos, 2023. "Estimating the contribution of CD4 T cell subset proliferation and differentiation to HIV persistence," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  33. Allassonnière, Stéphanie & Kuhn, Estelle, 2015. "Convergent stochastic Expectation Maximization algorithm with efficient sampling in high dimension. Application to deformable template model estimation," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 4-19.
  34. Li Cai, 2010. "High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 33-57, March.
  35. Boubacar Mainassara, Y. & Carbon, M. & Francq, C., 2012. "Computing and estimating information matrices of weak ARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 345-361.
  36. Marc Lavielle & Adeline Samson & Ana Karina Fermin & France Mentré, 2011. "Maximum Likelihood Estimation of Long-Term HIV Dynamic Models and Antiviral Response," Biometrics, The International Biometric Society, vol. 67(1), pages 250-259, March.
  37. Samson, Adeline & Lavielle, Marc & Mentre, France, 2006. "Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1562-1574, December.
  38. Fu, Liyong & Wang, Mingliang & Lei, Yuancai & Tang, Shouzheng, 2014. "Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 173-183.
  39. Trevezas, S. & Malefaki, S. & Cournède, P.-H., 2014. "Parameter estimation via stochastic variants of the ECM algorithm with applications to plant growth modeling," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 82-99.
  40. Commenges, D. & Jolly, D. & Drylewicz, J. & Putter, H. & Thiébaut, R., 2011. "Inference in HIV dynamics models via hierarchical likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 446-456, January.
  41. Cervellera, C. & Macciò, D., 2011. "A numerical method for minimum distance estimation problems," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 789-800, April.
  42. Sébastien Benzekry & Clare Lamont & Afshin Beheshti & Amanda Tracz & John M L Ebos & Lynn Hlatky & Philip Hahnfeldt, 2014. "Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-19, August.
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