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Discrepancy in regression estimates between log-normal and gamma: some case studies

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  • Rabindra Nath Das
  • Jeong-Soo Park

Abstract

In regression models with multiplicative error, estimation is often based on either the log-normal or the gamma model. It is well known that the gamma model with constant coefficient of variation and the log-normal model with constant variance give almost the same analysis. This article focuses on the discrepancies of the regression estimates between the two models based on real examples. It identifies that even though the variance or the coefficient of variation remains constant, but regression estimates may be different between the two models. It also identifies that for the same positive data set, the variance is constant under the log-normal model but non-constant under the gamma model. For this data set, the regression estimates are completely different between the two models. In the process, it explains the causes of discrepancies between the two models.

Suggested Citation

  • Rabindra Nath Das & Jeong-Soo Park, 2012. "Discrepancy in regression estimates between log-normal and gamma: some case studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 97-111, March.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:1:p:97-111
    DOI: 10.1080/02664763.2011.578618
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    References listed on IDEAS

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    1. R. Scott Hacker & Abdulnasser Hatemi-J, 2008. "Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(6), pages 601-615.
    2. Youngjo Lee & John A. Nelder, 2006. "Double hierarchical generalized linear models (with discussion)," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 139-185, April.
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    Cited by:

    1. Rabindra Nath Das & Amar Nath Shit & Apurba Ratan Ghosh, 2015. "Carp Seed Production Factors in India," Journal of Environments, Asian Online Journal Publishing Group, vol. 2(1), pages 10-17.
    2. Rabindra Nath Das & Anis Chandra Mukhopadhyay, 2017. "Correlated random effects regression analysis for a log-normally distributed variable," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(5), pages 897-915, April.
    3. Sara Gustavsson & Björn Fagerberg & Gerd Sallsten & Eva M. Andersson, 2014. "Regression Models for Log-Normal Data: Comparing Different Methods for Quantifying the Association between Abdominal Adiposity and Biomarkers of Inflammation and Insulin Resistance," IJERPH, MDPI, vol. 11(4), pages 1-19, March.

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