Relevant bibliographies by topics / Discrete choice

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Discrete choice'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Discrete choice"

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Vega-Bayo, Ainhoa, and Petr Mariel. "A Discrete Choice Experiment Application to School Choice." Revista Hacienda Pública Española 230, no. 3 (September 2019): 41–62. http://dx.doi.org/10.7866/hpe-rpe.19.3.2.

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Kerbler, Boštjan. "Discrete choice models." Urbani izziv 17, no. 1-2 (2006): 134–38. http://dx.doi.org/10.5379/urbani-izziv-en-2006-17-01-02-017.

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Hu, Ling, and Peter C. B. Phillips. "Nonstationary discrete choice." Journal of Econometrics 120, no. 1 (May 2004): 103–38. http://dx.doi.org/10.1016/s0304-4076(03)00208-2.

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Bordley, Robert. "Discrete choice with large choice sets." Economics Letters 118, no. 1 (January 2013): 13–15. http://dx.doi.org/10.1016/j.econlet.2012.05.010.

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Ramalho, E. A., and R. J. Smith. "Discrete Choice Non-Response." Review of Economic Studies 80, no. 1 (April 6, 2012): 343–64. http://dx.doi.org/10.1093/restud/rds018.

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ZHANG, JUNSEN, and SAUL D. HOFFMAN. "Discrete-Choice Logit Models." Sociological Methods & Research 22, no. 2 (November 1993): 193–213. http://dx.doi.org/10.1177/0049124193022002002.

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Anderson, Simon P., and André de Palma. "Reverse discrete choice models." Regional Science and Urban Economics 29, no. 6 (November 1999): 745–64. http://dx.doi.org/10.1016/s0166-0462(99)00009-5.

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Smirnov, Oleg A. "Modeling spatial discrete choice." Regional Science and Urban Economics 40, no. 5 (September 2010): 292–98. http://dx.doi.org/10.1016/j.regsciurbeco.2009.09.004.

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Nijkamp, Peter. "Discrete spatial choice analysis." Regional Science and Urban Economics 17, no. 1 (February 1987): 1–2. http://dx.doi.org/10.1016/0166-0462(87)90065-2.

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Ben-Akiva, Moshe, and Bruno Boccara. "Discrete choice models with latent choice sets." International Journal of Research in Marketing 12, no. 1 (May 1995): 9–24. http://dx.doi.org/10.1016/0167-8116(95)00002-j.

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Dissertations / Theses on the topic "Discrete choice"

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Boccara, Bruno 1956. "Modelling choice set formation in discrete choice models." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14324.

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Bonnet, Odran. "Individual housing choices and aggregate housing prices : discrete choice models revisited with matching models." Thesis, Paris, Institut d'études politiques, 2018. http://www.theses.fr/2018IEPP0010.

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Le premier chapitre, écrit conjointement avec Alfred Galichon, Keith O'Hara et Matthew Shum, montre l'équivalence entre les modèles de choix discrets et les modèles d'appariements. Cette équivalence permet l'estimation efficace, par des algorithmes d'appariement, de modèles qui étaient jusqu'à présent réputés comme difficile à estimer dans la littérature. Le deuxième chapitre, écrit conjointement avec Mathilde Poulhès, s'appuie sur les résultats du premier pour estimer le consentement marginal à payer des agents pour différentes caractéristiques du logement et du quartier à Paris. Il introduit une nouvelle procédure d'estimation basée sur le modèle de pures caractéristiques. Grâce à un riche jeu de données sur les achats de logements à Paris, nous montrons que le revenu moyen du voisinage et le niveau de criminalité sont de puissants déterminants du choix du quartier pour tous les types d'acheteurs, que l'accessibilité à l'emploi est également un facteur déterminant pour les ménages comptant plus d'une personne, et que la qualité de l'école du secteur joue un rôle primordial pour les ménages avec enfants. Le troisième chapitre, écrit conjointement avec Guillaume Chapelle, Alain Trannoy et Etienne Wasmer, montre que la croissance récente du ratio patrimoine sur revenu est due uniquement à l'augmentation du prix des logements, et plus précisément à l'augmentation du prix d'un facteur fixe de production: la terre. Nous montrons ensuite qu'un système de taxation du patrimoine doit taxer le facteur fixe qu'est la terre à des fins de redistribution et non le capital productif pour ne pas décourager l'investissement
The first two of the three chapters of this thesis examine the identification and the estimation of discrete choice models. The first chapter proves the equivalence between matching models and discrete choice models, and draws the consequences in terms of identification and estimation. The second chapter builds on the results of the first, and uses matching algorithms to estimate the marginal willingness to pay of households for various housing and neighborhood characteristics in Paris (such as school performance, crime level, distance to employment areas). The third chapter deals with another topic: it first shows that the recent rise in the capital-income ratio highlighted by Thomas Piketty in his book is due to the rise in housing prices, and it then explores the consequences in terms of wealth distribution
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Lindberg, Per Olov. "Contributions to Probabilistic Discrete Choice." Doctoral thesis, KTH, Transport- och lokaliseringsanalys, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-95402.

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Han, Bijun. "Analyzing car ownership and route choices using discrete choice models." Doctoral thesis, KTH, Infrastructure and Planning, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3215.

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This thesis consists of two parts. The first part analyzesthe accessibility, generation and license holding effects incar ownership models. The second part develops a route choicemodeling framework with an attempt to address the differencesin drivers' route choice behavior. These two parts of work areboth based on the discrete choice theory - the car ownershipmodels are built up on the standard logit model, whereas theroute choice models are formulated in a mixed logit form.

The study result of the first part shows that measuring theaccessibility by the monetary inclusive value reasonably wellcaptures the mechanism of the accessibility impact. Otheraccessibility proxies such as the parking costs, parking typeand house type are correlated with the accessibility but not toa great extent. Both young and old households are less likelyto have a car. The reduction of the propensity to own a car issignificant for households with average birth year before 1920,whereas this reduction is moderate for households with birthyear between 1920 and 1945. It is also demonstrated thatdriving license holding choice is conditional on the carownership level choice, and that these two choices need to bemodeled in a dynamic framework.

The second part of the work investigates the performance ofthe mixed logit model using both simulated data and empiricalroute switching data. The empirical study mainly focused on theimpacts of information and incident related factors on drivers'route switching behavior.

The result shows that using mixed logit gives a significantimprovement in model performance as well as a more sensitiveexplanation of drivers' decision-making behavior. For apopulation with greatly varying tastes, simply using thestandard logit model to analyze its behavior can yield veryunrealistic results. However, care must be taken when settingthe number of random draws for simulating the choiceprobability of the mixed logit model in order to get reliableestimates.

The empirical results demonstrate that incident relatedfactors such as delay and information reliability havesignificant impacts on drivers' route switching, where themagnitude of the response to the change in the delay is shownto vary significantly between individuals. Other factors, suchas confidence in the estimated delay, gender, frequency of cardriving and attitude towards congestion, also make majorcontributions. In addition, it is found that individual's routeswitching behavior may differ depending on the purpose of thetrip and when the choice is made, i.e. pre-trip oren-route.

Keywords: car ownership, accessibility, logit model,route choice, heterogeneity, mixed logit model

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Tapley, Nigel. "Nonlinearities in discrete choice attributes : a study of transport-related choices." Thesis, University of Leeds, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496518.

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Lukban, Albert. "Discrete choice modelling in conjoint analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0005/MQ44211.pdf.

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Lukban, Albert. "Discrete choice modelling in conjoint analysis." Thesis, McGill University, 1997. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=20582.

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Strategic planning is not only necessary in today's global economy where markets are becoming more susceptible to international competition, it is vital. The foresight of market reactions can lead to a competitive advantage. Market share losses can be minimized (and market share gains maximized) with the knowledge obtained from primary marketing research involving a stated preference study to examine consumer behaviour. Before launching a new product or providing a service, discrete choice analysis can empower strategic planners, managers and marketers with a tool which aids in optimizing products and services for a potential market with the end of maximizing sales and services.
Discrete choice analysis is a tool to understand human choice behaviour. It is employed for statistical inference on a model of choice behaviour from data obtained by sampling from a population of decision makers. This thesis gives an overview of the basic concepts of conjoint analysis which addresses discrete choice analysis for strategic product and service planning. The statistical model specification, the multinomial logit, is derived assuming that decision makers follow a choice rule called utility maximization, where these random utilities are Gumbel distributed. The model is applied to a stated preference study in which environmentally friendly vehicles are presented as possible vehicle choices.
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Nagel, Herbert, and Reinhold Hatzinger. "Diagnostics in some Discrete Choice Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1990. http://epub.wu.ac.at/506/1/document.pdf.

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Discrete choice models form a class of models widely used in econometrics for modelling the individual choice from a finite set of alternatives. The most widely used model is the multinomial logit model, implicitly assuming independence of irrelevant alternatives. A generalization is the nested multinomial logit model, relaxing this strong assurnp tion. Viewing both models as nonlinear regression models a set of diagnostics is derived. This includes a hat matrix, measures of leverage, influence and residuals and an approximation to the parameters for case deletion. In an example for the multinomid logit model a good performance of these diagnostics is observed and the parameter approximation by the proposed formula is better than a one step Newton-Raphson procedure. In an example for the nested logit model a constructed outlier with high influence is revealed by the measures of leverage and residual, but the parameter approximation is insufficient. (author's abstract)
Series: Forschungsberichte / Institut für Statistik
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Meginnis, Keila. "Strategic bias in discrete choice experiments." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/strategic-bias-in-discrete-choice-experiments(1a1407ed-c026-4d27-b336-3dfc69dba8d9).html.

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This thesis investigates the problem of strategic bias in discrete choice experiments using three approaches: simulations, a laboratory experiment, and a field experiment. Carson et al. (2007) state that choice experiments must be seen as consequential by respondents in order to provide useful information about preferences. To meet this requirement, surveys must exhibit two characteristics. First, the survey must be seen as influencing the provision of the good/service and second, the respondent must care about that provision outcome. However, the notion that the survey must be consequential, may also induce respondents to misrepresent their preferences in order to influence the decision making process. This is known as strategic bias. Using simulations, laboratory experiments and field studies, this thesis will investigate strategic bias in discrete choice experiments. To do so, we must assume that the discrete choice experiment will be used to influence a provisioning decision. Strategic behaviour is conceptualised as changes in choice behaviour that occurs when respondents have information on the relative likelihood of the provision outcomes. This information causes changes in choice strategies. In Chapter 1 we create a simulated environment that replicates when respondents may have incentive to bias their choice strategies. We explore the ramifications of biased choices through simulations. In Chapter 2 we replicate that environment in an induced value laboratory experiment. We present respondents with three possible `provision' outcomes, each of which was defined in terms of levels of arbitrary attributes. Each possible provision outcome was associated with a monetary payout to the respondent, therefore we know a respondents preference ordering over the possible outcomes. The monetary payouts varied such that there was a clear ranking between possible outcomes, such that respondents had a first, second and worst provision outcome. Respondents then completed a discrete choice experiment (DCE) that would decide which provision outcome is paid out. A respondent's payout was determined by which of the competing provision outcomes had the highest choice probability, based on their individual choice behaviour. As this was an unlabelled choice experiment, participants had to use choice strategies based on attributes in order to influence the provision outcome they hoped would get paid out. To introduce strategic bias, respondents were given information on the likelihood of the provision contenders being implemented. This information would affect which provision outcome would be chosen and hence paid out. In this respect, a respondent has incentive to act strategically if they believe (i) their first best outcome will lose in the final provision decision; (ii) their worst outcome is most likely to be paid out; and (iii) their choices can influence the provision to deliver the second best outcome. Strategic respondents are therefore those who chose to target their second best outcome to avoid worst outcome, which respondents are told is the most likely. We find approximately 27% of respondents exhibit strategic behaviour in the laboratory choice experiment. In Chapter 3 we implement the approach in the field. We administered a choice experiment about tidal energy development in Puget Sound, Washington. Local opposition to tidal energy has been strong in the past, with no project ever coming to fruition. As a result, we envision respondents to have strong sentiments towards the location of tidal energy development and potentially have incentives to behave strategically when location is a factor in their decision making process. We presented respondents with three possible site locations for tidal energy development in Puget Sound. Respondents can use the choice experiment as a means to reveal which site location they prefer (if any). As this was an unlabelled experiment, respondents must use choice strategies based on the attributes of tidal development in order to reveal preference for location. In order to identify strategic behaviour, rather than simply differences in preferences, half way through the DCE, we provide respondents with additional information on the likelihood of each of the tidal energy sites being chosen. Respondents whose first best location is revealed as the site least likely to be chosen, may seek to secure their second best location. Strategic bias is when respondents are trying to push preference away from their worst location site, when the set of provision outcomes has gone from three to two possible sites. Despite the complexity of the task, there is evidence of behavioural changes when the final set of possible locations changes. We find that 25% of respondents exhibit signs of strategic behaviour. These respondents are more likely to 1) believe their survey responses will affect future policy; and, 2) prefer the site which was revealed to be the unlikely site location and thus have strong motive to push development away from their worst location, and towards a second best site.
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Graham, Justin W. "School choice : a discrete optimization approach." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127294.

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Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 32-34).
An equitable and flexible mechanism for assigning students to schools is a major concern for many school districts. The school a student attends dramatically impacts the quality of education, access to resources, family and neighborhood cohesion, and transportation costs. Facing this intricate optimization problem, school districts often utilize to stable-matching techniques which only produce stable matchings that do not incorporate these different objectives; this can be expensive and inequitable. We present a new optimization model for the Stable Matching (SM) school choice problem which relies on an algorithm we call Price-Costs-Flexibility-and- Fairness (PCF2). Our model leverages techniques to balance competing objectives using mixed-integer optimization methods. We explore the trade-offs between stability, costs, and preferences and show that, surprisingly, there are stable solutions that decrease transportation costs by 8-17% over the Gale-Shapley solution.
by Justin W. Graham.
S.M.
S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center
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Books on the topic "Discrete choice"

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Zwerina, Klaus. Discrete Choice Experiments in Marketing. Heidelberg: Physica-Verlag HD, 1997. http://dx.doi.org/10.1007/978-3-642-50013-8.

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Börsch-Supan, Axel, ed. Econometric Analysis of Discrete Choice. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-45633-6.

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Páez, Antonio, and Geneviève Boisjoly. Discrete Choice Analysis with R. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20719-8.

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Discrete choice methods with simulation. 2nd ed. New York: Cambridge University Press, 2009.

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Mariel, Petr, David Hoyos, Jürgen Meyerhoff, Mikolaj Czajkowski, Thijs Dekker, Klaus Glenk, Jette Bredahl Jacobsen, et al. Environmental Valuation with Discrete Choice Experiments. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62669-3.

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Brocke, Michaela. Präferenzmessung durch die Discrete Choice-Analyse. Wiesbaden: Gabler, 2006. http://dx.doi.org/10.1007/978-3-8350-9046-0.

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Chorus, Caspar G. Random Regret-based Discrete Choice Modeling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29151-7.

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Chesher, Andrew. Taste variation in discrete choice models. Bristol: University of Bristol, Department of Economics, 1995.

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Andre, De Palma, and Thisse Jacques Franc̜ois, eds. Discrete choice theory of product differentiation. Cambridge, Mass: MIT Press, 1992.

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Nechyba, Thomas J. Community choice and local public services: A discrete choice approach. Cambridge, MA: National Bureau of Economic Research, 1997.

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Book chapters on the topic "Discrete choice"

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de Jong, Gerard, and Eric Kroes. "Discrete Choice Analysis." In Sxi — Springer per l’Innovazione / Sxi — Springer for Innovation, 121–42. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04786-7_8.

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Bougette, Patrice. "Discrete Choice Models." In Encyclopedia of Law and Economics, 571–72. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-7753-2_142.

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Bougette, Patrice. "Discrete Choice Models." In Encyclopedia of Law and Economics, 1–2. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4614-7883-6_142-1.

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Marcenaro-Gutierrez, Oscar. "Discrete Choice Analysis." In Encyclopedia of Quality of Life and Well-Being Research, 1661–63. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_748.

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Tockhorn-Heidenreich, Antje, Mandy Ryan, and Rodolfo Hernández. "Discrete Choice Experiments." In Patient Involvement in Health Technology Assessment, 121–33. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4068-9_10.

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Paczkowski, Walter R. "Discrete choice models." In Pricing Analytics, 105–31. 1 Edition. | New York : Routledge, 2018.: Routledge, 2018. http://dx.doi.org/10.4324/9781315178349-7.

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Amemiya, Takeshi. "Discrete Choice Models." In The New Palgrave Dictionary of Economics, 2910–18. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_86.

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Bierlaire, Michel. "Discrete Choice Models." In Operations Research and Decision Aid Methodologies in Traffic and Transportation Management, 203–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03514-6_9.

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Schroeder, Douglas A. "Discrete choice models." In Accounting and Causal Effects, 77–95. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7225-5_5.

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Temme, Jarg. "Discrete-Choice-Modelle." In Methodik der empirischen Forschung, 299–314. Wiesbaden: Gabler Verlag, 2009. http://dx.doi.org/10.1007/978-3-322-96406-9_20.

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Conference papers on the topic "Discrete choice"

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Tomlinson, Kiran, Johan Ugander, and Austin R. Benson. "Choice Set Confounding in Discrete Choice." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467378.

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Sharma, Pankaj, and Lav R. Varshney. "Rational Inattention in Choice Overload: Clustering for Discrete Choices." In 2021 55th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2021. http://dx.doi.org/10.1109/ieeeconf53345.2021.9723230.

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Labutov, Igor, Kelvin Luu, Hod Lipson, and Christoph Studer. "Optimally Discriminative Choice Sets in Discrete Choice Models." In L@S 2016: Third (2016) ACM Conference on Learning @ Scale. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2876034.2893394.

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Labutov, Igor, Frans Schalekamp, Kelvin Luu, Hod Lipson, and Christoph Studer. "Optimally Discriminative Choice Sets in Discrete Choice Models." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939879.

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Liu, Wenxi, Rynson Lau, and Dinesh Manocha. "Crowd simulation using Discrete Choice Model." In 2012 IEEE Virtual Reality (VR). IEEE, 2012. http://dx.doi.org/10.1109/vr.2012.6180866.

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Yang, Chen, Wei Wang, Zhibin Li, and Jian Lu. "Travel Mode Choice Based on Latent Variable Enriched Discrete Choice Model." In Second International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41039(345)720.

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Jifeng Dai, Haiyan Wu, and Jianxin Lin. "Discrete modal choice model in suburban districts." In 6th Advanced Forum on Transportation of China (AFTC 2010). IET, 2010. http://dx.doi.org/10.1049/cp.2010.1129.

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Benson, Austin R., Ravi Kumar, and Andrew Tomkins. "A Discrete Choice Model for Subset Selection." In WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3159652.3159702.

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Talarposhti, Maryam Sadeghi, Mohammad Ali Ahmadi-Pajouh, and Farzad Towhidkhah. "Modeling one-choice discrete-continuous dual task." In 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME). IEEE, 2020. http://dx.doi.org/10.1109/icbme51989.2020.9319428.

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Changjiang, Zheng, Gui Ling, and Yuan Li. "Study on the Choices of Park and Ride Based on Discrete Choice Models." In 2010 WASE International Conference on Information Engineering (ICIE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icie.2010.286.

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Reports on the topic "Discrete choice"

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Rosen, Adam, and Andrew Chesher. Structural modeling of simultaneous discrete choice. The IFS, February 2020. http://dx.doi.org/10.1920/wp.cem.2020.920.

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Nechyba, Thomas, and Robert Strauss. Community Choice and Local Public Services: A Discrete Choice Approach. Cambridge, MA: National Bureau of Economic Research, March 1997. http://dx.doi.org/10.3386/w5966.

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Heckman, James, and Salvador Navarro. Dynamic Discrete Choice and Dynamic Treatment Effects. Cambridge, MA: National Bureau of Economic Research, October 2005. http://dx.doi.org/10.3386/t0316.

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Brock, William, and Steven Durlauf. Discrete Choice with Social Interactions I: Theory. Cambridge, MA: National Bureau of Economic Research, October 1995. http://dx.doi.org/10.3386/w5291.

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Petrin, Amil, and Kenneth Train. Omitted Product Attributes in Discrete Choice Models. Cambridge, MA: National Bureau of Economic Research, January 2003. http://dx.doi.org/10.3386/w9452.

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Woodford, Michael. An Optimizing Neuroeconomic Model of Discrete Choice. Cambridge, MA: National Bureau of Economic Research, February 2014. http://dx.doi.org/10.3386/w19897.

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Chen, Le-Yu. Identification of structural dynamic discrete choice models. Institute for Fiscal Studies, May 2009. http://dx.doi.org/10.1920/wp.cem.2009.0809.

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Thirkettle, Matthew, Francesca Molinari, and Levon Barseghyan. Discrete choice under risk with limited consideration. The IFS, February 2019. http://dx.doi.org/10.1920/wp.cem.2019.08.

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Thirkettle, Matthew, Francesca Molinari, and Levon Barseghyan. Discrete choice under risk with limited consideration. The IFS, February 2019. http://dx.doi.org/10.1920/wp.cem.2019.0819.

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Thirkettle, Matthew, Francesca Molinari, and Levon Barseghyan. Discrete choice under risk with limited consideration. The IFS, June 2020. http://dx.doi.org/10.1920/wp.cem.2020.2820.

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