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Regression models for count data from truncated distributions

Author

Listed:
  • James W. Hardin

    (Department of Epidemiology and Biostatistics, University of South Carolina)

  • Joseph M. Hilbe

    (School of Social and Family Dynamics, Arizona State University)

Abstract

We present new commands for analyzing count-data regression models for truncated distributions. The trncregress command allows specification of a regression model for the mean of the truncated distribution through options. In addition to support for truncated Poisson and negative binomial, trncregress fits models based on truncated versions of distributions including generalized Poisson, Poisson-inverse Gaussian, three-parameter negative binomial power, three-parameter Waring negative binomial, and three-parameter Famoye negative binomial. Copyright 2015 by StataCorp LP.

Suggested Citation

  • James W. Hardin & Joseph M. Hilbe, 2015. "Regression models for count data from truncated distributions," Stata Journal, StataCorp LP, vol. 15(1), pages 226-246, March.
    • Handle: RePEc:tsj:stataj:v:15:y:2015:i:1:p:226-246
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    Cited by:

    1. Preusse, Verena & Wollni, Meike, 2021. "Adoption of sustainable agricultural practices in the context of urbanisation and environmental stress – Evidence from farmers in the rural-urban interface of Bangalore, India," 2021 Annual Meeting, August 1-3, Austin, Texas 312690, Agricultural and Applied Economics Association.
    2. Fátima Espinosa Casero & Luis César Herrero Prieto, 2024. "Valuing cultural heritage through non-monetary scales: A comparison," ACEI Working Paper Series AWP-02-2024, Association for Cultural Economics International.
    3. Ryan T. Godwin, 2024. "One-inflated zero-truncated count regression models," Papers 2402.02272, arXiv.org.
    4. Wang, Fang, 2024. "Does the recombination of distant scientific knowledge generate valuable inventions? An analysis of pharmaceutical patents," Technovation, Elsevier, vol. 130(C).
    5. Jun-You Lin, 2021. "Collaboration exploitation and exploration: does a proactive search strategy matter?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8295-8329, October.
    6. Siphiw’okuhle Nohamba & Joseph P. Musara & Yonas T. Bahta & Abiodun A. Ogundeji, 2022. "Drivers of Postharvest Loss among Citrus Farmers in Eastern Cape Province of South Africa: A Zero-Inflated Poisson (ZIP) Regression Model Analysis," Agriculture, MDPI, vol. 12(10), pages 1-19, October.
    7. Ana María Martínez-Rodríguez & Antonio Conde-Sánchez & María José Olmo-Jiménez, 2019. "A new approach to truncated regression for count data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 503-526, December.
    8. Hülya Ünlü & Serdal Temel & Kristel Miller, 2023. "Understanding the drivers of patent performance of University Science Parks in Turkey," The Journal of Technology Transfer, Springer, vol. 48(3), pages 842-872, June.

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