Technical Note—Dynamic Data-Driven Estimation of Nonparametric Choice Models

Published Online:https://doi.org/10.1287/opre.2020.2077

References

  • Ahmadi H , Shanbhag UV (2014) Data-driven first-order methods for misspecified convex optimization problems: Global convergence and rate estimates. Proc. 53rd IEEE Conf. Decision Control (Institute of Electrical and Electronics Engineers, Los Angeles, CA), 4228–4233.Google Scholar
  • Block H , Marschak J (1960) Random orderings and stochastic theories of response. Olkin I, Ghurye SG, Hoeffding W, Madow WG, Mann HB, eds. Contributions to probability and statistics; essays in honor of Harold Hotelling (Stanford University Press, Stanford, CA), 97–132.Google Scholar
  • Desir A , Goyal V , Jagabathula S , Segev D (2016) Assortment optimization under the Mallows model. Lee D, Sugiyama M, Luxburg U, Guyon I, Garnett R, eds. Proc. 30th Conf. Neural Inform. Processing Systems (Curran Associates, Inc., Red Hook, NY), 4707–4715.Google Scholar
  • Dwork C , Kumar R , Naor M , Sivakumar D (2001) Rank aggregation methods for the web. Proc. 10th Internat. Conf. World Wide Web (Association for Computing Machinery, New York, NY), 613–622.Google Scholar
  • Farias VF , Jagabathula S , Shah D (2009) A data-driven approach to modeling choice. Bengio Y, Schuurmans D, Lafferty J, Williams C, Culotta A, eds. Proc. 22nd Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 504–512.Google Scholar
  • Farias V , Jagabathula S , Shah D (2013) A nonparametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.LinkGoogle Scholar
  • Farias VF , Jagabathula S , Shah D (2017) Building optimized and hyperlocal product assortments: A nonparametric choice approach. Preprint, submitted January 24, http://dx.doi.org/10.2139/ssrn.2905381.Google Scholar
  • Freund RM , Grigas P (2016) New analysis and results for the Frank–Wolfe method. Math. Programming 155(1):199–230.CrossrefGoogle Scholar
  • Ho-Nguyen N , Kılınç-Karzan F (2019) Exploiting problem structure in optimization under uncertainty via online convex optimization. Math. Programming 177(1):113–147.CrossrefGoogle Scholar
  • Jagabathula S , Rusmevichientong P (2019) The limit of rationality in choice modeling: Formulation, computation, and implications. Management Sci. 65(5):2196–2215.AbstractGoogle Scholar
  • Jagabathula S , Shah D (2008) Inferring rankings under constrained sensing. Koller D, Schuurmans D, Bengio Y, Bottou L, eds. Proc. 21st Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 753–760.Google Scholar
  • Jiang H , Shanbhag UV (2016) On the solution of stochastic optimization and variational problems in imperfect information regimes. SIAM J. Optim. 26(4):2394–2429.CrossrefGoogle Scholar
  • Juditsky A , Nemirovski A (2012) First-order methods for nonsmooth convex large-scale optimization. I. General purpose methods. Sra S , Nowozin S , Wright S , eds. Optimization for Machine Learning , Neural Information Processing Series (MIT Press, Cambridge, MA), 121–148.Google Scholar
  • Mahajan S , van Ryzin G (2001) Stocking retail assortments under dynamic consumer substitution. Oper. Res. 49(3):334–351.LinkGoogle Scholar
  • Mišić VV (2016) Data, models and decisions for large-scale stochastic optimization. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA.Google Scholar
  • Nesterov Y (2018) Complexity bounds for primal-dual methods minimizing the model of objective function. Math. Programming 171(1):311–330.CrossrefGoogle Scholar
  • Rusmevichientong P , Roy BV , Glynn P (2006) A nonparametric approach to multiproduct pricing. Oper. Res. 54(1):82–98.LinkGoogle Scholar
  • Sion M (1958) On general minimax theorems. Pacific J. Math. 8(1):171–176.CrossrefGoogle Scholar
  • Talluri K , van Ryzin G (2005) The Theory and Practice of Revenue Management , International Series in Operations Research & Management Science, vol. 68 (Springer, New York).CrossrefGoogle Scholar
  • van Ryzin G , Vulcano G (2015) A market discovery algorithm to estimate a general class of nonparametric choice models. Management Sci. 61(2):281–300.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.