isotone

Isotone optimization in R: Pool-adjacent-violators algorithm (PAVA) and active set methods. In this paper we give a general framework for isotone optimization. First we discuss a generalized version of the pool-adjacent-violators algorithm (PAVA) to minimize a separable convex function with simple chain constraints. Besides of general convex functions we extend existing PAVA implementations in terms of observation weights, approaches for tie handling, and responses from repeated measurement designs. Since isotone optimization problems can be formulated as convex programming problems with linear constraints we the develop a primal active set method to solve such problem. This methodology is applied on specific loss functions relevant in statistics. Both approaches are implemented in the R package isotone


References in zbMATH (referenced in 33 articles , 1 standard article )

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  1. Al Mohamad, Diaa; van Zwet, Erik; Solari, Aldo; Goeman, Jelle: Simultaneous confidence intervals for ranks using the partitioning principle (2021)
  2. Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
  3. Dai, Ran; Song, Hyebin; Barber, Rina Foygel; Raskutti, Garvesh: The bias of isotonic regression (2020)
  4. Gao, Chao; Han, Fang; Zhang, Cun-Hui: On estimation of isotonic piecewise constant signals (2020)
  5. Hocking, Toby Dylan; Rigaill, Guillem; Fearnhead, Paul; Bourque, Guillaume: Constrained dynamic programming and supervised penalty learning algorithms for peak detection in genomic data (2020)
  6. Kim, Youngseok; Gao, Chao: Bayesian model selection with graph structured sparsity (2020)
  7. Vincent Runge, Toby Dylan Hocking, Gaetano Romano, Fatemeh Afghah, Paul Fearnhead, Guillem Rigaill: gfpop: an R Package for Univariate Graph-Constrained Change-point Detection (2020) arXiv
  8. Fang, Fang; Chen, Yuanyuan: A new approach for credit scoring by directly maximizing the Kolmogorov-Smirnov statistic (2019)
  9. Gudkov, Aleksandr Aleksandrovich; Mironov, Sergeĭ Vladimirovich; Sidorov, Sergeĭ Petrovich; Tyshkevich, Sergeĭ Viktorovich: A dual active set algorithm for optimal sparse convex regression (2019)
  10. Ozturk, Omer; Balakrishnan, Narayanaswamy: Constructing quantile confidence intervals using extended simple random sample in finite populations (2019)
  11. Colubi, Ana; Dominguez-Menchero, J. Santos; Gonzalez-Rodriguez, Gil: New designs to consistently estimate the isotonic regression (2018)
  12. Liu, Hao; Qin, Jing: Semiparametric probit models with univariate and bivariate current-status data (2018)
  13. Siriwardhana, Chathura; Kulasekera, K. B.; Datta, Somnath: Flexible semi-parametric regression of state occupational probabilities in a multistate model with right-censored data (2018)
  14. Burdakov, Oleg; Sysoev, Oleg: A dual active-set algorithm for regularized monotonic regression (2017)
  15. Moro, Russ A.; Härdle, Wolfgang K.; Schäfer, Dorothea: Company rating with support vector machines (2017)
  16. Casey Jelsema and Shyamal Peddada: CLME: An R Package for Linear Mixed Effects Models under Inequality Constraints (2016) not zbMATH
  17. Delgado, Miguel A.; Escanciano, Juan Carlos: Distribution-free tests of conditional moment inequalities (2016)
  18. Hofner, Benjamin; Kneib, Thomas; Hothorn, Torsten: A unified framework of constrained regression (2016)
  19. Kalish, Michael L.; Dunn, John C.; Burdakov, Oleg P.; Sysoev, Oleg: A statistical test of the equality of latent orders (2016)
  20. Vapnik, Vladimir; Izmailov, Rauf: Synergy of monotonic rules (2016)

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