Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first-order conditional linearization and sequential quadratic programming
Fu L, Wang M, Wang Z, Song X, Tang S. Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first-order conditional linearization and sequential quadratic programming. International Journal Of Biomathematics 2019, 12: 1950040. DOI: 10.1142/s1793524519500402.Peer-Reviewed Original ResearchSequential quadratic programmingNLME modelsMaximum likelihood estimationNonlinear mixed effects modelsParameter estimationQuadratic programmingGeneral formulationLikelihood estimationRandom effectsStandard statistical packagesVariance-covariance matrixModel linearizationMethod convergesConditional expansionComputational algorithmComputational optimizationNormal assumptionNLME modelingError termSimulation studyLinearizationMixed effects modelsEstimationHigh accuracyAlgorithm