IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v58y2009i5p619-639.html
   My bibliography  Save this article

Multivariate Weibull mixtures with proportional hazard restrictions for dwell‐time‐based session clustering with incomplete data

Author

Listed:
  • Patrick Mair
  • Marcus Hudec

Abstract

Summary. Emanating from classical Weibull mixture models we propose a framework for clustering survival data with various more parsimonious models by imposing restrictions on the distributional parameters. We show that these restrictions on the Weibull mixtures correspond to different proportional hazard restrictions across mixture components and Web page areas. A parametric cluster approach based on the EM algorithm is carried out on a multivariate data set. Our model set‐up encompasses incomplete‐data structures as well as censoring observations. We apply the methodology on retail data stemming from a global e‐commerce company. Sessions are clustered with respect to the dwell times that a user spends on certain page areas. The cluster solution that is found allows for a detailed examination of the navigation behaviour in terms of the hazard and survivor functions within each component.

Suggested Citation

  • Patrick Mair & Marcus Hudec, 2009. "Multivariate Weibull mixtures with proportional hazard restrictions for dwell‐time‐based session clustering with incomplete data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 619-639, December.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:5:p:619-639
    DOI: 10.1111/j.1467-9876.2009.00665.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9876.2009.00665.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1467-9876.2009.00665.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wendy W. Moe & Peter S. Fader, 2004. "Dynamic Conversion Behavior at E-Commerce Sites," Management Science, INFORMS, vol. 50(3), pages 326-335, March.
    2. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
    3. Eric J. Johnson & Wendy W. Moe & Peter S. Fader & Steven Bellman & Gerald L. Lohse, 2004. "On the Depth and Dynamics of Online Search Behavior," Management Science, INFORMS, vol. 50(3), pages 299-308, March.
    4. Makoto Abe, 2003. "A Two-Stage Prediction Model for Web Page Transition," CIRJE F-Series CIRJE-F-194, CIRJE, Faculty of Economics, University of Tokyo.
    5. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
    6. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    7. Zeileis, Achim, 2006. "Object-oriented Computation of Sandwich Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(i09).
    8. Honore, Bo E, 1990. "Simple Estimation of a Duration Model with Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 58(2), pages 453-473, March.
    9. Patra, Kaushik & Dey, Dipak K., 1999. "A multivariate mixture of Weibull distributions in reliability modeling," Statistics & Probability Letters, Elsevier, vol. 45(3), pages 225-235, November.
    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. Wei Zhao & Limin Peng & John Hanfelt, 2022. "Semiparametric latent class analysis of recurrent event data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1175-1197, September.
    2. Francesca Ieva & Anna Maria Paganoni & Teresa Pietrabissa, 2017. "Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure," Health Care Management Science, Springer, vol. 20(3), pages 353-364, September.
    3. Brian L. Egleston & Robert G. Uzzo & Yu-Ning Wong, 2017. "Latent Class Survival Models Linked by Principal Stratification to Investigate Heterogenous Survival Subgroups Among Individuals With Early-Stage Kidney Cancer," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 534-546, April.

    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. Amy Wenxuan Ding & Shibo Li & Patrali Chatterjee, 2015. "Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation," Information Systems Research, INFORMS, vol. 26(2), pages 339-359, June.
    2. Schröder, Nadine & Falke, Andreas & Hruschka, Harald & Reutterer, Thomas, 2019. "Analyzing the Browsing Basket: A Latent Interests-Based Segmentation Tool," Journal of Interactive Marketing, Elsevier, vol. 47(C), pages 181-197.
    3. Lizhen Xu & Jason A. Duan & Andrew Whinston, 2014. "Path to Purchase: A Mutually Exciting Point Process Model for Online Advertising and Conversion," Management Science, INFORMS, vol. 60(6), pages 1392-1412, June.
    4. Goh, Khim-Yong & Chu, Junhong & Wu, Jing, 2015. "Mobile Advertising: An Empirical Study of Temporal and Spatial Differences in Search Behavior and Advertising Response," Journal of Interactive Marketing, Elsevier, vol. 30(C), pages 34-45.
    5. Sahar Karimi, 2021. "Cross-visiting Behaviour of Online Consumers Across Retailers’ and Comparison Sites, a Macro-Study," Information Systems Frontiers, Springer, vol. 23(3), pages 531-542, June.
    6. Prabuddha De & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2010. "Technology Usage and Online Sales: An Empirical Study," Management Science, INFORMS, vol. 56(11), pages 1930-1945, November.
    7. Aishwarya Deep Shukla & Guodong (Gordon) Gao & Ritu Agarwal, 2021. "How Digital Word-of-Mouth Affects Consumer Decision Making: Evidence from Doctor Appointment Booking," Management Science, INFORMS, vol. 67(3), pages 1546-1568, March.
    8. Jura Liaukonyte & Thales Teixeira & Kenneth C. Wilbur, 2015. "Television Advertising and Online Shopping," Marketing Science, INFORMS, vol. 34(3), pages 311-330, May.
    9. Oliver Rutz & Randolph Bucklin, 2012. "Does banner advertising affect browsing for brands? clickstream choice model says yes, for some," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 231-257, June.
    10. Meihua Zuo & Spyros Angelopoulos & Zhouyang Liang & Carol X. J. Ou, 2023. "Blazing the Trail: Considering Browsing Path Dependence in Online Service Response Strategy," Information Systems Frontiers, Springer, vol. 25(4), pages 1605-1619, August.
    11. Park, Chang Hee, 2017. "Online Purchase Paths and Conversion Dynamics across Multiple Websites," Journal of Retailing, Elsevier, vol. 93(3), pages 253-265.
    12. Bucklin, Randolph E. & Sismeiro, Catarina, 2009. "Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 35-48.
    13. Sam K. Hui & Peter S. Fader & Eric T. Bradlow, 2009. "Path Data in Marketing: An Integrative Framework and Prospectus for Model Building," Marketing Science, INFORMS, vol. 28(2), pages 320-335, 03-04.
    14. Amit Bhatnagar & Arun Sen & Atish P. Sinha, 2017. "Providing a Window of Opportunity for Converting eStore Visitors," Information Systems Research, INFORMS, vol. 28(1), pages 22-32, March.
    15. Kris J. Ferreira & Sunanda Parthasarathy & Shreyas Sekar, 2022. "Learning to Rank an Assortment of Products," Management Science, INFORMS, vol. 68(3), pages 1828-1848, March.
    16. Benjamin Reed Shiller, 2020. "Approximating Purchase Propensities And Reservation Prices From Broad Consumer Tracking," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(2), pages 847-870, May.
    17. Hausman, Jerry A. & Woutersen, Tiemen, 2014. "Estimating a semi-parametric duration model without specifying heterogeneity," Journal of Econometrics, Elsevier, vol. 178(P1), pages 114-131.
    18. Cecere, Grazia & Le Guel, Fabrice & Soulié, Nicolas, 2012. "Perceived Internet privacy concerns on social network in Europe," MPRA Paper 41437, University Library of Munich, Germany.
    19. Ayala, Luis & Rodriguez, Magdalena, 2007. "Barriers to employment and welfare dynamics: Evidence from Spain," Journal of Policy Modeling, Elsevier, vol. 29(2), pages 237-257.
    20. Bo E. Honore & Aureo de Paula, 2007. "Interdependent Durations, Second Version," PIER Working Paper Archive 08-044, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Nov 2008.

    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:bla:jorssc:v:58:y:2009:i:5:p:619-639. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.