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A Disaggregate Negative Binomial Regression Procedure for Count Data Analysis

Author

Listed:
  • Venkatram Ramaswamy

    (School of Business Administration, The University of Michigan, Ann Arbor, Michigan 48109)

  • Eugene W. Anderson

    (School of Business Administration, The University of Michigan, Ann Arbor, Michigan 48109)

  • Wayne S. DeSarbo

    (School of Business Administration, The University of Michigan, Ann Arbor, Michigan 48109)

Abstract

Various research areas face the methodological problems presented by nonnegative integer count data drawn from heterogeneous populations. We present a disaggregate negative binomial regression procedure for analysis of count data observed for a heterogeneous sample of cross-sections, possibly over some fixed time periods. This procedure simultaneously pools or groups cross-sections while estimating a separate negative binomial regression model for each group. An E-M algorithm is described within a maximum likelihood framework to estimate the group proportions, the group-specific regression coefficients, and the degree of overdispersion in event rates within each derived group. The proposed procedure is illustrated with count data entailing nonnegative integer counts of purchases (events) for a frequently bought consumer good.

Suggested Citation

  • Venkatram Ramaswamy & Eugene W. Anderson & Wayne S. DeSarbo, 1994. "A Disaggregate Negative Binomial Regression Procedure for Count Data Analysis," Management Science, INFORMS, vol. 40(3), pages 405-417, March.
    • Handle: RePEc:inm:ormnsc:v:40:y:1994:i:3:p:405-417
    DOI: 10.1287/mnsc.40.3.405
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    Cited by:

    1. van Oest, R.D. & Paap, R., 2004. "Analyzing the effects of past prices on reference price formation," Econometric Institute Research Papers EI 2004-36, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Jaideep Anand, 2004. "Redeployment of corporate resources: A study of acquisition strategies in the US defense industries, 1978-1996," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 25(6-7), pages 383-400.
    3. Simon Blanchard & Wayne DeSarbo, 2013. "A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification," Psychometrika, Springer;The Psychometric Society, vol. 78(2), pages 322-340, April.
    4. Xia, Yanchun & Qiao, Zhilin & Xie, Guanghua, 2022. "Corporate resilience to the COVID-19 pandemic: The role of digital finance," Pacific-Basin Finance Journal, Elsevier, vol. 74(C).

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