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Monotonicity of quadratic-approximation algorithms

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  • Dankmar Böhning
  • Bruce Lindsay

Abstract

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Suggested Citation

  • Dankmar Böhning & Bruce Lindsay, 1988. "Monotonicity of quadratic-approximation algorithms," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 40(4), pages 641-663, December.
    • Handle: RePEc:spr:aistmt:v:40:y:1988:i:4:p:641-663
    DOI: 10.1007/BF00049423
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    Citations

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    Cited by:

    1. Takayuki Kawashima & Hironori Fujisawa, 2023. "Robust regression against heavy heterogeneous contamination," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(4), pages 421-442, May.
    2. Liu, Wenchen & Tang, Yincai & Wu, Xianyi, 2020. "Separating variables to accelerate non-convex regularized optimization," Computational Statistics & Data Analysis, Elsevier, vol. 147(C).
    3. de Leeuw, Jan & Lange, Kenneth, 2009. "Sharp quadratic majorization in one dimension," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2471-2484, May.
    4. Bissantz, Nicolai & Dümbgen, Lutz & Munk, Axel & Stratmann, Bernd, 2008. "Convergence analysis of generalized iteratively reweighted least squares algorithms on convex function spaces," Technical Reports 2008,25, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Nicolas Depraetere & Martina Vandebroek, 2017. "A comparison of variational approximations for fast inference in mixed logit models," Computational Statistics, Springer, vol. 32(1), pages 93-125, March.
    6. Lee, Sangin & Kwon, Sunghoon & Kim, Yongdai, 2016. "A modified local quadratic approximation algorithm for penalized optimization problems," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 275-286.
    7. Roussille, Nina & Scuderi, Benjamin, 2023. "Bidding for Talent: A Test of Conduct in a High-Wage Labor Market," IZA Discussion Papers 16352, Institute of Labor Economics (IZA).
    8. Tian, Guo-Liang & Ma, Huijuan & Zhou, Yong & Deng, Dianliang, 2015. "Generalized endpoint-inflated binomial model," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 97-114.
    9. Wang, Fa, 2017. "Maximum likelihood estimation and inference for high dimensional nonlinear factor models with application to factor-augmented regressions," MPRA Paper 93484, University Library of Munich, Germany, revised 19 May 2019.
    10. Dankmar Böhning, 1992. "Multinomial logistic regression algorithm," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(1), pages 197-200, March.
    11. de Leeuw, Jan, 2006. "Principal component analysis of binary data by iterated singular value decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 21-39, January.
    12. Kenneth Lange & Hua Zhou, 2022. "A Legacy of EM Algorithms," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 52-66, December.
    13. Utkarsh J. Dang & Michael P.B. Gallaugher & Ryan P. Browne & Paul D. McNicholas, 2023. "Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 145-167, April.
    14. Bansal, Prateek & Daziano, Ricardo A & Guerra, Erick, 2018. "Minorization-Maximization (MM) algorithms for semiparametric logit models: Bottlenecks, extensions, and comparisons," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 17-40.
    15. Tian, Guo-Liang & Tang, Man-Lai & Liu, Chunling, 2012. "Accelerating the quadratic lower-bound algorithm via optimizing the shrinkage parameter," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 255-265.
    16. Jonathan James, 2012. "A tractable estimator for general mixed multinomial logit models," Working Papers (Old Series) 1219, Federal Reserve Bank of Cleveland.
    17. Wang, Fa, 2022. "Maximum likelihood estimation and inference for high dimensional generalized factor models with application to factor-augmented regressions," Journal of Econometrics, Elsevier, vol. 229(1), pages 180-200.
    18. Ding, Jieli & Tian, Guo-Liang & Yuen, Kam Chuen, 2015. "A new MM algorithm for constrained estimation in the proportional hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 135-151.
    19. Amadou Sawadogo & Simplice Dossou-Gbété & Dominique Lafon, 2017. "Ties in one block comparison experiments: a generalization of the Mallows–Bradley–Terry ranking model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2621-2644, October.
    20. Bohning, Dankmar, 1999. "The lower bound method in probit regression," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 13-17, March.
    21. Tian, Guo-Liang & Tang, Man-Lai & Fang, Hong-Bin & Tan, Ming, 2008. "Efficient methods for estimating constrained parameters with applications to regularized (lasso) logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3528-3542, March.
    22. Durante, Daniele & Canale, Antonio & Rigon, Tommaso, 2019. "A nested expectation–maximization algorithm for latent class models with covariates," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 97-103.

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