IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v42y2008i10p958-969.html
   My bibliography  Save this article

Normalization of network generalized extreme value models

Author

Listed:
  • Newman, Jeffrey P.

Abstract

Generalized extreme value (GEV) models provide a convenient way to model choice behavior that is consistent with utility maximization theory, but the development of specific new models within the GEV family has been slow, due to the difficulty of ensuring new formulations comply with all the GEV rules. The network GEV structure introduced by Daly and Bierlaire [Daly, A., Bierlaire, M., 2006. A general and operational representation of generalised extreme value models. Transportation Research Part B 40, 285-305] provides a tool to quickly generate new models in the GEV family, without the burden of complex analysis of the new model to ensure its properties. This paper further develops the network GEV tool, describing several methodologies for correctly normalizing the allocation parameters in such models, to ensure unbiasedness. These methods vary depending on the structure of the underlying network.

Suggested Citation

  • Newman, Jeffrey P., 2008. "Normalization of network generalized extreme value models," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 958-969, December.
    • Handle: RePEc:eee:transb:v:42:y:2008:i:10:p:958-969
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191-2615(08)00043-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Small, Kenneth A, 1987. "A Discrete Choice Model for Ordered Alternatives," Econometrica, Econometric Society, vol. 55(2), pages 409-424, March.
    2. Coldren, Gregory M. & Koppelman, Frank S., 2005. "Modeling the competition among air-travel itinerary shares: GEV model development," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(4), pages 345-365, May.
    3. Koppelman, Frank S. & Wen, Chieh-Hua, 1998. "Alternative nested logit models: structure, properties and estimation," Transportation Research Part B: Methodological, Elsevier, vol. 32(5), pages 289-298, June.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    5. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    6. Abbe, E. & Bierlaire, M. & Toledo, T., 2007. "Normalization and correlation of cross-nested logit models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 795-808, August.
    7. Swait, Joffre, 2003. "Flexible Covariance Structures for Categorical Dependent Variables through Finite Mixtures of Generalized Extreme Value Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 80-87, January.
    8. Daly, Andrew & Bierlaire, Michel, 2006. "A general and operational representation of Generalised Extreme Value models," Transportation Research Part B: Methodological, Elsevier, vol. 40(4), pages 285-305, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aswin Kannan & Uday V. Shanbhag, 2019. "Optimal stochastic extragradient schemes for pseudomonotone stochastic variational inequality problems and their variants," Computational Optimization and Applications, Springer, vol. 74(3), pages 779-820, December.
    2. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.
    3. Marzano, Vittorio, 2014. "A simple procedure for the calculation of the covariances of any Generalized Extreme Value model," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 151-162.
    4. Papola, Andrea, 2016. "A new random utility model with flexible correlation pattern and closed-form covariance expression: The CoRUM," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 80-96.
    5. Tinessa, Fiore, 2021. "Closed-form random utility models with mixture distributions of random utilities: Exploring finite mixtures of qGEV models," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 262-288.
    6. Newman, Jeffrey P. & Ferguson, Mark E. & Garrow, Laurie A., 2013. "Estimating GEV models with censored data," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 170-184.
    7. Marzano, Vittorio & Papola, Andrea & Simonelli, Fulvio & Vitillo, Roberta, 2013. "A practically tractable expression of the covariances of the Cross-Nested Logit model," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 1-11.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.
    2. Tinessa, Fiore, 2021. "Closed-form random utility models with mixture distributions of random utilities: Exploring finite mixtures of qGEV models," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 262-288.
    3. Marzano, Vittorio & Papola, Andrea & Simonelli, Fulvio & Vitillo, Roberta, 2013. "A practically tractable expression of the covariances of the Cross-Nested Logit model," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 1-11.
    4. Marzano, Vittorio, 2014. "A simple procedure for the calculation of the covariances of any Generalized Extreme Value model," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 151-162.
    5. Papola, Andrea, 2016. "A new random utility model with flexible correlation pattern and closed-form covariance expression: The CoRUM," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 80-96.
    6. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2022. "Submodularity and local search approaches for maximum capture problems under generalized extreme value models," European Journal of Operational Research, Elsevier, vol. 300(3), pages 953-965.
    7. Marzano, Vittorio & Papola, Andrea, 2008. "On the covariance structure of the Cross-Nested Logit model," Transportation Research Part B: Methodological, Elsevier, vol. 42(2), pages 83-98, February.
    8. Peter Davis & Pasquale Schiraldi, 2014. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products," RAND Journal of Economics, RAND Corporation, vol. 45(1), pages 32-63, March.
    9. Newman, Jeffrey P. & Lurkin, Virginie & Garrow, Laurie A., 2018. "Computational methods for estimating multinomial, nested, and cross-nested logit models that account for semi-aggregate data," Journal of choice modelling, Elsevier, vol. 26(C), pages 28-40.
    10. Mai, Tien, 2016. "A method of integrating correlation structures for a generalized recursive route choice model," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 146-161.
    11. Sener, Ipek N. & Pendyala, Ram M. & Bhat, Chandra R., 2011. "Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior," Journal of Transport Geography, Elsevier, vol. 19(2), pages 294-303.
    12. Perez-Lopez, Jose-Benito & Novales, Margarita & Orro, Alfonso, 2022. "Spatially correlated nested logit model for spatial location choice," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 1-12.
    13. Drabas, Tomasz & Wu, Cheng-Lung, 2013. "Modelling air carrier choices with a Segment Specific Cross Nested Logit model," Journal of Air Transport Management, Elsevier, vol. 32(C), pages 8-16.
    14. Bekhor, Shlomo & Prashker, Joseph N., 2008. "GEV-based destination choice models that account for unobserved similarities among alternatives," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 243-262, March.
    15. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    16. Koo, Tay T.R. & Hossein Rashidi, Taha & Park, Jin-Woo & Wu, Cheng-Lung & Tseng, Wen-Chun, 2017. "The effect of enhanced international air access on the demand for peripheral tourism destinations: Evidence from air itinerary choice behaviour of Korean visitors to Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 116-129.
    17. Emerson Melo, 2021. "Learning in Random Utility Models Via Online Decision Problems," Papers 2112.10993, arXiv.org, revised Aug 2022.
    18. Laura Grigolon, 2021. "Blurred boundaries: A flexible approach for segmentation applied to the car market," Quantitative Economics, Econometric Society, vol. 12(4), pages 1273-1305, November.
    19. José-Benito Pérez-López & Margarita Novales & Francisco-Alberto Varela-García & Alfonso Orro, 2020. "Residential Location Econometric Choice Modeling with Irregular Zoning: Common Border Spatial Correlation Metric," Networks and Spatial Economics, Springer, vol. 20(3), pages 785-802, September.
    20. Mogens Fosgerau & Julien Monardo & André de Palma, 2019. "The Inverse Product Differentiation Logit Model," Working Papers hal-02183411, HAL.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transb:v:42:y:2008:i:10:p:958-969. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.