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Semiparametric estimation of heterogeneous count data models

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  • Brannas, Kurt
  • Rosenqvist, Gunnar

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  • Brannas, Kurt & Rosenqvist, Gunnar, 1994. "Semiparametric estimation of heterogeneous count data models," European Journal of Operational Research, Elsevier, vol. 76(2), pages 247-258, July.
    • Handle: RePEc:eee:ejores:v:76:y:1994:i:2:p:247-258
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    Citations

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    Cited by:

    1. Marco Alfò & Giovanni Trovato, 2004. "Semiparametric Mixture Models for Multivariate Count Data, with Application," CEIS Research Paper 51, Tor Vergata University, CEIS.
    2. Óscar D. Lourenço & Pedro L. Ferreira, 2005. "Utilization of public health centres in Portugal: effect of time costs and other determinants. Finite mixture models applied to truncated samples," Health Economics, John Wiley & Sons, Ltd., vol. 14(9), pages 939-953, September.
    3. Lim, Hwa Kyung & Li, Wai Keung & Yu, Philip L.H., 2014. "Zero-inflated Poisson regression mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 151-158.
    4. Nadarajah, Saralees & Kotz, Samuel, 2009. "Models for purchase frequency," European Journal of Operational Research, Elsevier, vol. 192(3), pages 1014-1026, February.
    5. William Greene, 2004. "Convenient estimators for the panel probit model: Further results," Empirical Economics, Springer, vol. 29(1), pages 21-47, January.
    6. Andersson, Thomas & Brännäs, Kurt, 1991. "Explaining Cross-Country Variation in Nationalization Frequencies," Working Paper Series 319, Research Institute of Industrial Economics.
    7. Marco Alfò & Cecilia Vitiello, 2003. "Finite mixtures approach to ecological regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 93-108, February.
    8. Greene, W., 2001. "Fixed and Random Effects in Nonlinear Models," New York University, Leonard N. Stern School Finance Department Working Paper Seires 01-01, New York University, Leonard N. Stern School of Business-.
    9. Glenn Ellison & Ashley Swanson, 2012. "Heterogeneity in High Math Achievement Across Schools: Evidence from the American Mathematics Competitions," NBER Working Papers 18277, National Bureau of Economic Research, Inc.
    10. Payandeh Najafabadi Amir T. & MohammadPour Saeed, 2018. "A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 12(2), pages 1-31, July.
    11. Glenn Ellison & Ashley Swanson, 2016. "Do Schools Matter for High Math Achievement? Evidence from the American Mathematics Competitions," American Economic Review, American Economic Association, vol. 106(6), pages 1244-1277, June.
    12. Lundevaller, Erling Häggström, 2009. "The effect of travel cost on frequencies of shopping and recreational trips in Sweden," Journal of Transport Geography, Elsevier, vol. 17(3), pages 208-215.
    13. Alessandro Lomi, 1993. "A semiparametric random effects approach to unobserved heterogeneity in count data models of organizational founding," Quality & Quantity: International Journal of Methodology, Springer, vol. 27(2), pages 139-155, May.

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