IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v87y2003i2p356-369.html
   My bibliography  Save this article

Factor models for multivariate count data

Author

Listed:
  • Wedel, Michel
  • Böckenholt, Ulf
  • Kamakura, Wagner A.

Abstract

We develop a general class of factor-analytic models for the analysis of multivariate (truncated) count data. Dependencies in multivariate counts are of interest in many applications, but few approaches have been proposed for their analysis. Our model class allows for a variety of distributions of the factors in the exponential family. The proposed framework includes a large number of previously proposed factor and random effect models as special cases and leads to many new models that have not been considered so far. Whereas previously these models were proposed separately as different cases, our framework unifies these models and enables one to study them simultaneously. We estimate the Poisson factor models with the method of simulated maximum likelihood. A Monte-Carlo study investigates the performance of this approach in terms of estimation bias and precision. We illustrate the approach in an analysis of TV channels data.

Suggested Citation

  • Wedel, Michel & Böckenholt, Ulf & Kamakura, Wagner A., 2003. "Factor models for multivariate count data," Journal of Multivariate Analysis, Elsevier, vol. 87(2), pages 356-369, November.
    • Handle: RePEc:eee:jmvana:v:87:y:2003:i:2:p:356-369
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(03)00020-4
    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. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    2. Chib, Siddhartha & Winkelmann, Rainer, 2001. "Markov Chain Monte Carlo Analysis of Correlated Count Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 428-435, October.
    3. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    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. Robert C. Jung & Roman Liesenfeld & Jean-François Richard, 2011. "Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 73-85, January.
    2. Kim, Hea-Jung & Choi, Taeryon & Jo, Seongil, 2016. "Bayesian factor analysis with uncertain functional constraints about factor loadings," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 110-128.
    3. Y Chen & X Li, 2022. "Determining the number of factors in high-dimensional generalized latent factor models [Eigenvalue ratio test for the number of factors]," Biometrika, Biometrika Trust, vol. 109(3), pages 769-782.
    4. Bijwaard, Govert E. & Franses, Philip Hans, 2009. "The effect of rounding on payment efficiency," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1449-1461, February.
    5. Peter Congdon, 2011. "The Spatial Pattern of Suicide in the US in Relation to Deprivation, Fragmentation and Rurality," Urban Studies, Urban Studies Journal Limited, vol. 48(10), pages 2101-2122, August.
    6. Fokianos, Konstantinos, 2024. "Multivariate Count Time Series Modelling," Econometrics and Statistics, Elsevier, vol. 31(C), pages 100-116.
    7. Chen, Yunxiao & Li, Xiaoou, 2022. "Determining the number of factors in high-dimensional generalized latent factor models," LSE Research Online Documents on Economics 111574, London School of Economics and Political Science, LSE Library.
    8. Brooke E. Magnus & David Thissen, 2017. "Item Response Modeling of Multivariate Count Data With Zero Inflation, Maximum Inflation, and Heaping," Journal of Educational and Behavioral Statistics, , vol. 42(5), pages 531-558, October.
    9. Rolf Larsson, 2020. "Discrete factor analysis using a dependent Poisson model," Computational Statistics, Springer, vol. 35(3), pages 1133-1152, September.
    10. David I. Warton, 2011. "Regularized Sandwich Estimators for Analysis of High-Dimensional Data Using Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 67(1), pages 116-123, March.
    11. Carrie B. Myers & Scott M. Myers & Martha Peters, 2019. "The Longitudinal Connections Between Undergraduate High Impact Curriculum Practices and Civic Engagement in Adulthood," Research in Higher Education, Springer;Association for Institutional Research, vol. 60(1), pages 83-110, February.

    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. Daniela Andreini & Diego Rinallo & Giuseppe Pedeliento & Mara Bergamaschi, 2017. "Brands and Religion in the Secularized Marketplace and Workplace: Insights from the Case of an Italian Hospital Renamed After a Roman Catholic Pope," Journal of Business Ethics, Springer, vol. 141(3), pages 529-550, March.
    2. Byrd, T. A. & Marshall, T. E., 1997. "Relating information technology investment to organizational performance: a causal model analysis," Omega, Elsevier, vol. 25(1), pages 43-56, February.
    3. Golob, Thomas F. & Regan, A C, 2002. "Trucking Industry Preferences for Driver Traveler Information Using Wireless Internet-enabled Devices," University of California Transportation Center, Working Papers qt40q8h6sf, University of California Transportation Center.
    4. Naiara Escalante Mateos & Eider Goñi Palacios & Arantza Fernández-Zabala & Iratxe Antonio-Agirre, 2020. "Internal Structure, Reliability and Invariance across Gender Using the Multidimensional School Climate Scale PACE-33," IJERPH, MDPI, vol. 17(13), pages 1-24, July.
    5. Jung, Hyekyung & Schafer, Joseph L. & Seo, Byungtae, 2011. "A latent class selection model for nonignorably missing data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 802-812, January.
    6. repec:jss:jstsof:06:i02 is not listed on IDEAS
    7. Lin Ting Hsiang, 2006. "A comparison of model selection indices for nested latent class models," Monte Carlo Methods and Applications, De Gruyter, vol. 12(3), pages 239-259, October.
    8. Pendharkar, Parag C., 2006. "Scale economies and production function estimation for object-oriented software component and source code documentation size," European Journal of Operational Research, Elsevier, vol. 172(3), pages 1040-1050, August.
    9. Koufteros, Xenophon & Lu, Guanyi & Peters, Richard C. & Lai, Kee-hung & Wong, Christina W.Y. & Edwin Cheng, T.C., 2014. "Product development practices, manufacturing practices, and performance: A mediational perspective," International Journal of Production Economics, Elsevier, vol. 156(C), pages 83-97.
    10. Alfò, Marco & Rocchetti, Irene, 2013. "A flexible approach to finite mixture regression models for multivariate mixed responses," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1754-1758.
    11. Yang, Chih-Chien, 2006. "Evaluating latent class analysis models in qualitative phenotype identification," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1090-1104, February.
    12. Ando, Tomohiro, 2009. "Bayesian factor analysis with fat-tailed factors and its exact marginal likelihood," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1717-1726, September.
    13. GONZALO, Jesus & PITARAKIS, Jean-Yves, 1994. "Comovements in Large Systems," LIDAM Discussion Papers CORE 1994065, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Qi Yuan & Tee Hng Tan & Peizhi Wang & Fiona Devi & Hui Lin Ong & Edimansyah Abdin & Magadi Harish & Richard Goveas & Li Ling Ng & Siow Ann Chong & Mythily Subramaniam, 2020. "Staging dementia based on caregiver reported patient symptoms: Implications from a latent class analysis," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-12, January.
    15. Marc Blais & Ursula Hess & Andrea S. Riddle, 2002. "Static Versus Dynamic Structural Models of Depression: The Case of the CES-D," CIRANO Working Papers 2002s-37, CIRANO.
    16. Tuan, Luu Trong & Ngan, Vu Thanh, 2021. "Leading ethically to shape service-oriented organizational citizenship behavior among tourism salespersons: Dual mediation paths and moderating role of service role identity," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    17. Qi Chen & Wen Luo & Gregory J. Palardy & Ryan Glaman & Amber McEnturff, 2017. "The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored," SAGE Open, , vol. 7(1), pages 21582440177, March.
    18. Roy Levy & Gregory R. Hancock, 2011. "An Extended Model Comparison Framework for Covariance and Mean Structure Models, Accommodating Multiple Groups and Latent Mixtures," Sociological Methods & Research, , vol. 40(2), pages 256-278, May.
    19. Munzel, Andreas & Meyer-Waarden, Lars & Galan, Jean-Philippe, 2018. "The social side of sustainability: Well-being as a driver and an outcome of social relationships and interactions on social networking sites," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 14-27.
    20. Arantzazu Rodríguez-Fernández & Iker Izar-de-la-Fuente & Naiara Escalante & Lorea Azpiazu, 2021. "Perceived Social Support for a Sustainable Adolescence: A Theoretical Model of Its Sources and Types," Sustainability, MDPI, vol. 13(10), pages 1-13, May.
    21. Golob, Thomas F. & Regan, Amelia C., 2003. "Surveying and Modeling Trucking Industry Perceptions, Preferences and Behavior," University of California Transportation Center, Working Papers qt1gw166zk, University of California Transportation Center.

    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:jmvana:v:87:y:2003:i:2:p:356-369. 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/622892/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.