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A latent class model for obesity

Author

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  • Greene, William
  • Harris, Mark N.
  • Hollingsworth, Bruce
  • Maitra, Pushkar
Abstract
We extend the discrete data latent class literature by explicitly defining a latent variable for class membership as a function of both observables and unobservables, thereby allowing the equations defining the class membership and observed outcomes to be correlated. The procedure is then applied to modelling observed obesity outcomes, based upon an underlying ordered probit equation.

Suggested Citation

  • Greene, William & Harris, Mark N. & Hollingsworth, Bruce & Maitra, Pushkar, 2014. "A latent class model for obesity," Economics Letters, Elsevier, vol. 123(1), pages 1-5.
    • Handle: RePEc:eee:ecolet:v:123:y:2014:i:1:p:1-5
    DOI: 10.1016/j.econlet.2014.01.004
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    References listed on IDEAS

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    1. Stephen Pudney & Michael Shields, 2000. "Gender, race, pay and promotion in the British nursing profession: estimation of a generalized ordered probit model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(4), pages 367-399.
    2. Deb, Partha & Trivedi, Pravin K., 2002. "The structure of demand for health care: latent class versus two-part models," Journal of Health Economics, Elsevier, vol. 21(4), pages 601-625, July.
    3. Bago d'Uva, Teresa & Jones, Andrew M. & van Doorslaer, Eddy, 2009. "Measurement of horizontal inequity in health care utilisation using European panel data," Journal of Health Economics, Elsevier, vol. 28(2), pages 280-289, March.
    4. Teresa Bago d'Uva, 2006. "Latent class models for utilisation of health care," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 329-343, April.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. William H. Greene & Mark N. Harris & Rachel J. Knott & Nigel Rice, 2021. "Specification and testing of hierarchical ordered response models with anchoring vignettes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 31-64, January.
    2. repec:zbw:rwirep:0537 is not listed on IDEAS
    3. R. Todd Jewell, 2017. "Technical efficiency with multi-output, heterogeneous production: a latent class, distance function model of english football," Journal of Productivity Analysis, Springer, vol. 48(1), pages 37-50, August.
    4. Zhou, Heng & Norman, Richard & Xia, Jianhong(Cecilia) & Hughes, Brett & Kelobonye, Keone & Nikolova, Gabi & Falkmer, Torbjorn, 2020. "Analysing travel mode and airline choice using latent class modelling: A case study in Western Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 187-205.
    5. Işıl Şirin Selçuk & Altuğ Murat Köktaş & Şükrü Anıl Toygar, 2023. "Socioeconomic factors affecting the probability of obesity: evidence from a nationwide survey in Turkey," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 239-255, February.
    6. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    7. William Greene & Mark N. Harris & Bruce Hollingsworth & Rachel Knott & Nigel Rice, 2016. "Reporting heterogeneity effects in modelling self reports of health," Working Papers 16-12, New York University, Leonard N. Stern School of Business, Department of Economics.
    8. Sarah Brown & William Greene & Mark Harris, 2020. "A novel approach to latent class modelling: identifying the various types of body mass index individuals," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 983-1004, June.
    9. Brown, Sarah & Greene, William H. & Harris, Mark N., 2014. "A New Formulation for Latent Class Models," IZA Discussion Papers 8283, Institute of Labor Economics (IZA).
    10. Gulay Avsar & Roger Ham & W. Kathy Tannous, 2017. "Factors Influencing the Incidence of Obesity in Australia: A Generalized Ordered Probit Model," IJERPH, MDPI, vol. 14(2), pages 1-13, February.
    11. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Comparisons of observed and unobserved parameter heterogeneity in modeling vehicle-miles driven," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    12. Kairies-Schwarz, Nadja & Kokot, Johanna & Vomhof, Markus & Wessling, Jens, 2014. "How Do Consumers Choose Health Insurance? – An Experiment on Heterogeneity in Attribute Tastes and Risk Preferences," Ruhr Economic Papers 537, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    13. Padma Sharma, 2022. "Assessing Regulatory Responses to Banking Crises," Research Working Paper RWP 22-04, Federal Reserve Bank of Kansas City.
    14. Zhang, Rong & Inder, Brett A. & Zhang, Xibin, 2015. "Bayesian estimation of a discrete response model with double rules of sample selection," Computational Statistics & Data Analysis, Elsevier, vol. 86(C), pages 81-96.
    15. Nadja Kairies-Schwarz & Johanna Kokot & Markus Vomhof & Jens Wessling, 2014. "How Do Consumers Choose Health Insurance? – An Experiment on Heterogeneity in Attribute Tastes and Risk Preferences," Ruhr Economic Papers 0537, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.

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