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Exact Logit-Based Product Design
[article]
2021
arXiv
pre-print
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At the heart of our methodology is the surprising result that the logit-based SOCPD problem can be exactly reformulated as a mixed-integer convex program. ...
In this paper, we develop an exact methodology for solving this problem based on modern integer, convex and conic optimization. ...
Acknowledgments The authors thank the authors of Toubia et al. (2003) , Hainmueller et al. (2014) and Allenby and Ginter (1995) for making their data sets available, which were used in the experiments ...
arXiv:2106.15084v1
fatcat:4ud527p7jjhyfb5dcenqsiq3c4
Optimizing Offer Sets in Sub-Linear Time
[article]
2020
arXiv
pre-print
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Our algorithm works for an extremely general class of problems and models of user choice that includes the mixed multinomial logit model as a special case. ...
Thus motivated, we propose an algorithm for personalized offer set optimization that runs in time sub-linear in the number of items while enjoying a uniform performance guarantee. ...
For example, taking the 's to be Gumbel random variables yields the multinomial logit model, and allowing for random U yields the mixed multinomial logit. ...
arXiv:2011.08606v1
fatcat:4lorwousdfdozl4cyq5w7x3erq
Frequentist model averaging for multinomial and ordered logit models
2014
International Journal of Forecasting
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a r t i c l e i n f o Keywords: Asymptotic squared error risk Local mis-specification Model screening Monte Carlo Plug-in estimator a b s t r a c t Multinomial and ordered Logit models are quantitative ...
Our strategy of weight choice for the candidate models is based on the minimization of a plug-in estimator of the asymptotic squared error risk of the model average estimator. ...
Acknowledgments The authors thank the editor Graham Elliot, the associate editor and two referees for very helpful comments and suggestions. ...
doi:10.1016/j.ijforecast.2013.07.013
fatcat:wionh2mopzgwxjsqnuv763q7ri
Regularized Ordinal Regression and the ordinalNet R Package
2021
Journal of Statistical Software
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Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class. ...
perform variable selection. ...
Acknowledgments The authors thank Alex Tahk for the suggestion that led them to explore shrinking the nonparallel model to the parallel model. ...
doi:10.18637/jss.v099.i06
pmid:34512213
pmcid:PMC8432594
fatcat:go5si5pturbolgde6utshw6sem
A Generalized Markov Chain Model to Capture Dynamic Preferences and Choice Overload
[article]
2020
arXiv
pre-print
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Assortment optimization is an important problem that arises in many industries such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products ...
While we show that the assortment optimization under this model is NP-hard, we present fully polynomial-time approximation scheme (FPTAS) under reasonable assumptions. ...
For example, one could potentially estimate a different logit or mixed logit model for each assortment offered to consumers, which would allow the preference parameters to vary with assortment. ...
arXiv:1911.06716v4
fatcat:wijawsx5iramnbtfj7wknelnam
Regularized Ordinal Regression and the ordinalNet R Package
[article]
2017
arXiv
pre-print
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Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class. ...
perform variable selection. ...
Acknowledgements The authors thank Alex Tahk for the suggestion that led them to explore shrinking the nonparallel model to the parallel model. ...
arXiv:1706.05003v1
fatcat:mibuxhr22vfbdhxgrldwpz5a4y
Feature selection for high-dimensional temporal data
2018
BMC Bioinformatics
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Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize ...
Conclusions: The use of this algorithm is suggested for variable selection with high-dimensional temporal data. ...
Acknowledgements The first author would like to express his acknowledgments to Dimitris Rizopoulos and Janice Scealy for answering some of his questions. ...
doi:10.1186/s12859-018-2023-7
pmid:29357817
pmcid:PMC5778658
fatcat:6jjgibl3rfbp5n3kfgn4svpvli
JointAI: Joint Analysis and Imputation of Incomplete Data in R
[article]
2020
arXiv
pre-print
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JointAI provides functions for Bayesian inference with generalized linear and generalized linear mixed models and extensions thereof as well as survival models and joint models for longitudinal and survival ...
Usage and features of JointAI are described and illustrated using various examples and the theoretical background is outlined. ...
Multinomial logit (mixed) models Multinomial logit mixed models are implemented as y ij ∼ Mult(π ij,1 , . . . , π ij,K ), π ij,k = φ i,k / K q=1 φ i,q , k ∈ 1, . . . , K, log(φ ij,1 ) = 0, log(φ ij,k ) ...
arXiv:1907.10867v3
fatcat:x2fw7p6usva6fn7le6eanmzp2y
Multivariate Fractional Regression Estimation of Econometric Share Models
2015
Journal of Econometric Methods
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AbstractThis paper describes and applies econometric strategies for estimating regression models of economic share data outcomes where the shares may take boundary values (zero and 1) with nontrivial probability ...
"Econometric Methods for Fractional Response Variables with an Application to 401(k) Plan Participation Rates." ...
In addition, Badi Baltagi and Jeff Wooldridge provided some helpful guidance with the literature. All these colleagues, of course, are absolved from any blame for the paper's shortcomings. ...
doi:10.1515/jem-2012-0006
pmid:30079291
pmcid:PMC6075740
fatcat:rrorkq5hjzaz3njjp5jptt7ciu
Pricing structure optimization in mixed restricted/unrestricted fare environments
2010
Journal of Revenue and Pricing Management
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To that end, we formulate the problem as a dynamic program and approximate it with a mixed integer linear program (MIP) that selects the best price points out of a potentially large set of price candidates ...
We develop choice-based network revenue management approaches for such a mixed fare environment that can handle both the traditional opening or closing of restricted fare classes as well as handling pricing ...
A choice model suited to this task is the Multinomial Logit (MNL) with overlapping consideration sets, see Miranda Bront et al. (2009) , for example. ...
doi:10.1057/rpm.2010.33
fatcat:xnmjnruyirev5a4ai5hcq3dp6y
Multi-agent Assortment Optimization in Sequential Matching Markets
[article]
2021
arXiv
pre-print
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Suppliers observe the subset of consumers that selected them, and choose either to match a consumer or leave the system. ...
Given the computational complexity of the problem, we show several constant factor guarantees for the general model that, in particular, significantly improve the approximation factors previously obtained ...
Acknowledgements The second author thanks the support of the Institut de valorisation des données (IVADO) and Fonds de recherche du Québec (FRQ) through the FRQ-IVADO Research Chair and of the Natural ...
arXiv:2006.04313v2
fatcat:bbkz3o3jvnet3cfado5onfdtui
Choice Set Optimization Under Discrete Choice Models of Group Decisions
[article]
2020
arXiv
pre-print
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Here, we use discrete choice modeling to develop an optimization framework of such interventions for several problems of group influence, namely maximizing agreement or disagreement and promoting a particular ...
We design approximation algorithms for the hard problems and show that they work well on real-world choice data. ...
We thank Johan Ugander for helpful conversations. ...
arXiv:2002.00421v2
fatcat:lq3rcr67vjf6bcbtqkvzg5gxrq
Activity-based Market Equilibrium for Capacitated Multimodal Transport Systems
2015
Transportation Research Procedia
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The model is based on a constrained mixed logit model of activity schedule choice, where each schedule in the choice set is generated with a multimodal extension of the Household Activity Pattern Problem ...
The market equilibrium is achieved with Lagrangian relaxation to determine the optimal dual price of the capacity constraint, and a method of successive averages with column generation finds an efficient ...
The authors wish to thank the University of Toronto's Data Management Group for the Transportation Tomorrow Survey data used in this study, and to Susan Jia Xu for her help preparing some initial data ...
doi:10.1016/j.trpro.2015.06.001
fatcat:vthj6xlry5bubi34sir75rspd4
Data‐driven research in retail operations—A review
2020
Naval Research Logistics
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We then conclude the paper by pointing out some interesting future research possibilities for our community. ...
We survey state-of-the-art studies in three core aspects of retail operations-assortment optimization, order fulfillment, and inventory management. ...
ACKNOWLEDGMENTS The authors gratefully acknowledge feedback from Professor Ming Hu (Editor-in-Chief), the Associate Editor, and two anonymous referees, which have helped improve this article. ...
doi:10.1002/nav.21949
fatcat:lktqqpvc3fhebglrmtb3xjp7mq
Bayesian inference for categorical data analysis
2005
Statistical Methods & Applications
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Adopted usually in a hierarchical form, the logit-normal approach allows greater flexibility and scope for generalization. The 1970s also saw considerable interest in loglinear modeling. ...
The advent of modern computational methods since the mid-1980s has led to a growing literature on fully Bayesian analyses with models for categorical data, with main emphasis on generalized linear models ...
The authors thank Jon Forster for many helpful comments and suggestions about an earlier draft and thank him and Steve Fienberg and George Casella for suggesting relevant references. ...
doi:10.1007/s10260-005-0121-y
fatcat:3ya3smjjzfagxgx6q6bicp43rq
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