International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Joint GLM of Mean and Dispersion for the Determinants of Child Mortality

Author Affiliations

  • 1Department of Statistics, S.D.N.B Vaishnav College for Women chromepet, Chennai-44, Tamil Nadu, India
  • 2Department of Statistics, S.D.N.B Vaishnav College for Women chromepet, Chennai-44, Tamil Nadu, India

Res. J. Mathematical & Statistical Sci., Volume 4, Issue (2), Pages 1-5, March,12 (2016)

Abstract

Joint modelling of the mean and dispersion parameters as a function of explanatory variables is widely used for positive data analysis. Log - Gaussian or Gamma models for constant variance and Joint GLM for non- constant variance (Mc Cullagh and Nelder, 1989) are reported in literature. The aim of this paper is to apply this technique to identify the effects of some determinants in child mortality using data from NFHS-3 (2005-06). It is found that in modelling structured dispersion, Log - Gaussian model is better than Gamma model based on Akaike Information Criterion (AIC).

References

  1. OECD (2016), Child Mortality:, http://www.oecd.org/social/family/ database.html
  2. Researchgate (2016), Infant Mortality:, https://www.researchgate.net/ publication/20261350 Infant mortality.
  3. Zee News (2016), Infant mortality rate, - http://zeenews.india.com/tags/ Infant-Mortality-Rate.html
  4. Bharat Rakshak (2016), Indian Health Care Sector:, http://forums.bharat-rakshak.com/viewtopic.php?t=5375andstart=440
  5. Researchgate (2016), Socioeconomic Determinants of Infant Mortality Rate in India:, https://www.researchgate.net/profile/Manak Singariya/publication/236900863 Socioeconomic Determinants of Infant Mortality Rate in India/links/0deec51a2c83298b3f000000.pdf?origin=publication detail
  6. Rabindranath Das etal (2011), Infant Mortality in India: Evaluating Log-Gaussian and Gamma Distributions,, The open Demography Journal, 4, 34-41
  7. Firth D. (1988), Multiplicative errors: log-normal or Gamma?, Journal of Royal Statistical Society, 50(4), 266-268.
  8. Mc Gullah P. and Nelder J.A. (1989), Generalized Linear Models,, Chapman and Hall, London.
  9. Myers R.H., Montgomery D.C. and Vining G.G. (2010)., Generalized Linear Models with Application in Engineering and Sciences., John Wiley and Sons, New York.
  10. Das (2014)., Robust response surfaces regression and positive data analyses,, CRC press, London, New York.
  11. Grover G., Sabharwal A and Mittal J. (2013)., A Bayesian approach for estimating onset time of nephropathy for type 2 diabetic patients under various health conditions., IJSP, 2(2), 89-101.
  12. Lee Y. and Nelder J.A. (2003)., Robust designs via Generalized linear models., Journal Qual. Tech., 35(1), 2-12.
  13. Lee Y. and Nelder J.A. (1998)., Generalized linear models for the analysis of quality improvement experiments., Can J Stat, 26(1), 95-105.
  14. Rchiips (2016), NFHS:, http://rchiips.org/nfhs