AMLET is an acronym for Another Mixed Logit Estimation Tool. AMLET is software designed to estimate multinomial and mixed logit discrete choices models, which are increasingly popular in econometry. The software supports cross- sectional and panel data, and offers various optimization methods, including the new variable sample-size approach. (Source:

References in zbMATH (referenced in 15 articles , 1 standard article )

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  1. Jerinkić, Nataša Krklec; Rožnjik, Andrea: Penalty variable sample size method for solving optimization problems with equality constraints in a form of mathematical expectation (2020)
  2. Krejić, Nataša; Krklec Jerinkić, Nataša: Spectral projected gradient method for stochastic optimization (2019)
  3. Bhat, Chandra R.; Lavieri, Patrícia S.: A new mixed MNP model accommodating a variety of dependent non-normal coefficient distributions (2018)
  4. Birgin, E. G.; Krejić, N.; Martínez, J. M.: On the employment of inexact restoration for the minimization of functions whose evaluation is subject to errors (2018)
  5. Krejić, Nataša; Krklec Jerinkić, Nataša; Rožnjik, Andrea: Variable sample size method for equality constrained optimization problems (2018)
  6. Krejić, Nataša; Martínez, J. M.: Inexact restoration approach for minimization with inexact evaluation of the objective function (2016)
  7. Krejić, Nataša; Krklec Jerinkić, Nataša: Nonmonotone line search methods with variable sample size (2015)
  8. Royset, Johannes O.: On sample size control in sample average approximations for solving smooth stochastic programs (2013)
  9. Byrd, Richard H.; Chin, Gillian M.; Nocedal, Jorge; Wu, Yuchen: Sample size selection in optimization methods for machine learning (2012)
  10. Sabino, Piergiacomo: Implementing quasi-Monte Carlo simulations with linear transformations (2011)
  11. Bastin, Fabian; Malmedy, Vincent; Mouffe, Mélodie; Toint, Philippe L.; Tomanos, Dimitri: A retrospective trust-region method for unconstrained optimization (2010)
  12. Kaebe, C.; Maruhn, J. H.; Sachs, E. W.: Adjoint-based Monte Carlo calibration of financial methods (2009)
  13. Bastin, Fabian; Cirillo, Cinzia; Toint, Philippe L.: An adaptive Monte Carlo algorithm for computing mixed logit estimators (2006)
  14. Bastin, Fabian; Cirillo, Cinzia; Toint, Philippe L.: Convergence theory for nonconvex stochastic programming with an application to mixed logit (2006)
  15. Cherchi, Elisabetta; de Dios Ortúzar, Juan: Income, time effects and direct preferences in a multimodal choice context: application of mixed RP/SP models with nonlinear utilities (2006)