File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Persistency model and its applications in choice modeling

TitlePersistency model and its applications in choice modeling
Authors
KeywordsChoice functions
Integer programming
Probability distribution
Utility preference
Issue Date2009
PublisherI N F O R M S. The Journal's web site is located at http://mansci.pubs.informs.org
Citation
Management Science, 2009, v. 55 n. 3, p. 453-469 How to Cite?
AbstractGiven a discrete maximization problem with a linear objective function where the coefficients are chosen randomly from a distribution, we would like to evaluate the expected optimal value and the marginal distribution of the optimal solution. We call this the persistency problem for a discrete optimization problem under uncertain objective, and the marginal probability mass function of the optimal solution is named the persistence value. In general, this is a difficult problem to solve, even if the distribution of the objective coefficient is well specified. In this paper, we solve a subclass of this problem when the distribution is assumed to belong to the class of distributions defined by given marginal distributions, or given marginal moment conditions. Under this model, we show that the persistency problem maximizing the expected objective value over the set of distributions can be solved via a concave maximization model. The persistency model solved using this formulation can be used to obtain important qualitative insights to the behavior of stochastic discrete optimization problems. We demonstrate how the approach can be used to obtain insights to problems in discrete choice modeling. Using a set of survey data from a transport choice modeling study, we calibrate the random utility model with choice probabilities obtained from the persistency model. Numerical results suggest that our persistency model is capable of obtaining estimates that perform as well, if not better, than classical methods, such as logit and cross-nested logit models. We can also use the persistency model to obtain choice probability estimates for more complex choice problems. We illustrate this on a stochastic knapsack problem, which is essentially a discrete choice problem under budget constraint.
Persistent Identifierhttp://hdl.handle.net/10722/137305
ISSN
2015 Impact Factor: 2.741
2015 SCImago Journal Rankings: 4.384
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorNatarajan, Ken_HK
dc.contributor.authorTeo, CPen_HK
dc.date.accessioned2011-08-26T14:22:55Z-
dc.date.available2011-08-26T14:22:55Z-
dc.date.issued2009en_HK
dc.identifier.citationManagement Science, 2009, v. 55 n. 3, p. 453-469en_HK
dc.identifier.issn0025-1909en_HK
dc.identifier.urihttp://hdl.handle.net/10722/137305-
dc.description.abstractGiven a discrete maximization problem with a linear objective function where the coefficients are chosen randomly from a distribution, we would like to evaluate the expected optimal value and the marginal distribution of the optimal solution. We call this the persistency problem for a discrete optimization problem under uncertain objective, and the marginal probability mass function of the optimal solution is named the persistence value. In general, this is a difficult problem to solve, even if the distribution of the objective coefficient is well specified. In this paper, we solve a subclass of this problem when the distribution is assumed to belong to the class of distributions defined by given marginal distributions, or given marginal moment conditions. Under this model, we show that the persistency problem maximizing the expected objective value over the set of distributions can be solved via a concave maximization model. The persistency model solved using this formulation can be used to obtain important qualitative insights to the behavior of stochastic discrete optimization problems. We demonstrate how the approach can be used to obtain insights to problems in discrete choice modeling. Using a set of survey data from a transport choice modeling study, we calibrate the random utility model with choice probabilities obtained from the persistency model. Numerical results suggest that our persistency model is capable of obtaining estimates that perform as well, if not better, than classical methods, such as logit and cross-nested logit models. We can also use the persistency model to obtain choice probability estimates for more complex choice problems. We illustrate this on a stochastic knapsack problem, which is essentially a discrete choice problem under budget constraint.en_HK
dc.languageengen_US
dc.publisherI N F O R M S. The Journal's web site is located at http://mansci.pubs.informs.orgen_HK
dc.relation.ispartofManagement Scienceen_HK
dc.subjectChoice functionsen_HK
dc.subjectInteger programmingen_HK
dc.subjectProbability distributionen_HK
dc.subjectUtility preferenceen_HK
dc.titlePersistency model and its applications in choice modelingen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0025-1909&volume=55&issue=3&spage=453&epage=469&date=2009&atitle=Persistency+model+and+its+applications+in+choice+modeling-
dc.identifier.emailTeo, CP:msong@hku.hken_HK
dc.identifier.authorityTeo, CP=rp01375en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1287/mnsc.1080.0951en_HK
dc.identifier.scopuseid_2-s2.0-67649965668en_HK
dc.identifier.hkuros191408en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-67649965668&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume55en_HK
dc.identifier.issue3en_HK
dc.identifier.spage453en_HK
dc.identifier.epage469en_HK
dc.identifier.eissn1526-5501-
dc.identifier.isiWOS:000264089200009-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridNatarajan, K=14032276300en_HK
dc.identifier.scopusauthoridTeo, CP=36761612800en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats