IDEAS home Printed from https://ideas.repec.org/a/eee/matsoc/v43y2002i3p429-449.html
   My bibliography  Save this article

Two-level linear paired comparison models: estimation and identifiability issues

Author

Listed:
  • Tsai, Rung-Ching
  • Bockenholt, Ulf

Abstract

No abstract is available for this item.

Suggested Citation

  • Tsai, Rung-Ching & Bockenholt, Ulf, 2002. "Two-level linear paired comparison models: estimation and identifiability issues," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 429-449, July.
    • Handle: RePEc:eee:matsoc:v:43:y:2002:i:3:p:429-449
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165-4896(02)00019-7
    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. Norman T. Gridgeman, 1960. "Statistics and Taste Testing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 9(2), pages 103-112, June.
    2. Michael Browne, 1992. "Circumplex models for correlation matrices," Psychometrika, Springer;The Psychometric Society, vol. 57(4), pages 469-497, December.
    3. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," Department of Economics, Working Paper Series qt3tb6j874, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    4. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    5. Munizaga, Marcela A. & Heydecker, Benjamin G. & Ortúzar, Juan de Dios, 2000. "Representation of heteroskedasticity in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 34(3), pages 219-240, April.
    6. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    7. B. R. Dansie, 1986. "Normal Order Statistics as Permutation Probability Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(3), pages 269-275, November.
    8. Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
    9. R. Darrell Bock, 1958. "Remarks on the test of significance for the method of paired comparisons," Psychometrika, Springer;The Psychometric Society, vol. 23(4), pages 323-334, December.
    10. Ulf Böckenholt & William Dillon, 1997. "Modeling within-subject dependencies in ordinal paired comparison data," Psychometrika, Springer;The Psychometric Society, vol. 62(3), pages 411-434, September.
    11. Rung-Ching Tsai, 2000. "Remarks on the identifiability of thurstonian ranking models: Case V, case III, or neither?," Psychometrika, Springer;The Psychometric Society, vol. 65(2), pages 233-240, June.
    12. Evans, Michael & Swartz, Timothy, 2000. "Approximating Integrals via Monte Carlo and Deterministic Methods," OUP Catalogue, Oxford University Press, number 9780198502784, Decembrie.
    13. Vijverberg, Wim P. M., 1997. "Monte Carlo evaluation of multivariate normal probabilities," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 281-307.
    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. Steven Andrew Culpepper & James Joseph Balamuta, 2017. "A Hierarchical Model for Accuracy and Choice on Standardized Tests," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 820-845, September.
    2. Ulf Böckenholt, 2006. "Thurstonian-Based Analyses: Past, Present, and Future Utilities," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 615-629, December.

    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. Zsolt Sandor, 2009. "Multinomial discrete choice models (in Russian)," Quantile, Quantile, issue 7, pages 9-19, September.
    2. Andreas Ziegler, 2010. "Individual Characteristics and Stated Preferences for Alternative Energy Sources and Propulsion Technologies in Vehicles: A Discrete Choice Analysis," CER-ETH Economics working paper series 10/125, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    3. Andreas Ziegler, 2007. "Simulated classical tests in multinomial probit models," Statistical Papers, Springer, vol. 48(4), pages 655-681, October.
    4. Ziegler Andreas, 2010. "Z-Tests in Multinomial Probit Models under Simulated Maximum Likelihood Estimation: Some Small Sample Properties," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 230(5), pages 630-652, October.
    5. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    6. Daniel McFadden, 2001. "Economic Choices," American Economic Review, American Economic Association, vol. 91(3), pages 351-378, June.
    7. Baltas, George, 2004. "A model for multiple brand choice," European Journal of Operational Research, Elsevier, vol. 154(1), pages 144-149, April.
    8. Dubey, Subodh & Bansal, Prateek & Daziano, Ricardo A. & Guerra, Erick, 2020. "A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 114-141.
    9. Dahlberg, Matz & Eklöf, Matias, 2003. "Relaxing the IIA Assumption in Locational Choice Models: A Comparison Between Conditional Logit, Mixed Logit, and Multinomial Probit Models," Working Paper Series 2003:9, Uppsala University, Department of Economics.
    10. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    11. Brownstone, David, 2001. "Discrete Choice Modeling for Transportation," University of California Transportation Center, Working Papers qt29v7d1pk, University of California Transportation Center.
    12. Subodh Dubey & Prateek Bansal & Ricardo A. Daziano & Erick Guerra, 2019. "A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel," Papers 1904.08332, arXiv.org, revised Jan 2020.
    13. Algers, S. & Bergstrom, P. & Dahlberg, M. & Dillen, J.L., 1998. "Mixed Logit Estimation of the Value of Travel Time," Papers 1998:15, Uppsala - Working Paper Series.
    14. Patil, Priyadarshan N. & Dubey, Subodh K. & Pinjari, Abdul R. & Cherchi, Elisabetta & Daziano, Ricardo & Bhat, Chandra R., 2017. "Simulation evaluation of emerging estimation techniques for multinomial probit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 9-20.
    15. Mark D. Agee, 2020. "Classroom management, persistent bullying, and teacher practices in a discrete choice model of habit formation," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 4(1), pages 5-16, December.
    16. Baltas, George & Saridakis, Charalampos, 2013. "An empirical investigation of the impact of behavioural and psychographic consumer characteristics on car preferences: An integrated model of car type choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 54(C), pages 92-110.
    17. Karthik K. Srinivasan & Hani S. Mahmassani, 2005. "A Dynamic Kernel Logit Model for the Analysis of Longitudinal Discrete Choice Data: Properties and Computational Assessment," Transportation Science, INFORMS, vol. 39(2), pages 160-181, May.
    18. Yeonbae Kim & Tai-Yoo Kim & Eunnyeong Heo, 2003. "Bayesian estimation of multinomial probit models of work trip choice," Transportation, Springer, vol. 30(3), pages 351-365, August.
    19. Maydeu-Olivares, Albert, 2002. "Limited information estimation and testing of Thurstonian models for preference data," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 467-483, July.
    20. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.

    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:matsoc:v:43:y:2002:i:3:p:429-449. 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/locate/inca/505565 .

    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.