Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2018, Cilt: 47 Sayı: 2, 463 - 470, 01.04.2018

Öz

Kaynakça

  • Davidian, M. and Giltinan D. M.. Nonlinear Models for Repeated Measurement Data, (London: Chapman and Hall, 1995).
  • McCullagh, P., Quasi-likelihood function, The Annals of Statistics 11, 59-67, 1983.
  • McCullagh, P.and Nelder, J. A., Generalized Linear Models, 2nd edn., ( London: Chapman and Hall, 1989).
  • McCulloch, C. E.and Searle, S. R., Generalized Linear and Mixed Models, (New York: Wiley, 2001).
  • Mielke, T., Approximations of the Fisher Information for the construction of Ecient Experimental Designs in Nonlinear Mixed Eects Models, (Ph.D. Thesis, Otto-von-Guericke University, Germany, 2012).
  • Niaparast, M., On optimal design for a Poisson regression model with random intercept, Statistics and Probability Letters, textbf79, 741-747, 2009.
  • Niaparast, M. and Schwabe, R., Optimal design for quasi-likelihood estimation in Poisson regression with random coecients, Journal of Statistical Planning and Inference, 143, 296- 306, 2013.
  • Pinheiro, J. C. and Bates D. M., Mixed-Eects Models in S and S-Plus, (New York: Springer, 2000).
  • Pukelsheim, F. (1993). Optimal design of Experiments, (New York: Wiley, 1993).
  • Stufken, J. and Yang, M., On locally Optimal designs for Generalized Linear Models with group eects, The statistica sinica, 22, 1756-1786, 2012.
  • Waterhouse, T. H. (2005). Optimal Experimental Design for Nonlinear and Generalised Linear Models, (PhD thesis, University of Queensland, Australia, 2005).

Efficiency of D-optimal designs for quasi-likelihood estimation in Poisson regression model with random effects

Yıl 2018, Cilt: 47 Sayı: 2, 463 - 470, 01.04.2018

Öz

Optimum experimental designs are most commonly used to obtain maximum likelihood estimators of parameters. However, obtaining an explicit form of these estimators is not feasible for generalized linear mixed models (GLMMs). Hence as an alternative method to handle this issue, the quasi-likelihood method is applied to Poisson regression models with random effects, a special case of GLMMs. In this paper, we consider this model and compare D-optimal designs for quasi-likelihood estimation and maximum likelihood estimation of fixed effects parameters. The empirical results in a simulated environment suggest that the optimal designs for quasi-likelihood estimation are efficient.

Kaynakça

  • Davidian, M. and Giltinan D. M.. Nonlinear Models for Repeated Measurement Data, (London: Chapman and Hall, 1995).
  • McCullagh, P., Quasi-likelihood function, The Annals of Statistics 11, 59-67, 1983.
  • McCullagh, P.and Nelder, J. A., Generalized Linear Models, 2nd edn., ( London: Chapman and Hall, 1989).
  • McCulloch, C. E.and Searle, S. R., Generalized Linear and Mixed Models, (New York: Wiley, 2001).
  • Mielke, T., Approximations of the Fisher Information for the construction of Ecient Experimental Designs in Nonlinear Mixed Eects Models, (Ph.D. Thesis, Otto-von-Guericke University, Germany, 2012).
  • Niaparast, M., On optimal design for a Poisson regression model with random intercept, Statistics and Probability Letters, textbf79, 741-747, 2009.
  • Niaparast, M. and Schwabe, R., Optimal design for quasi-likelihood estimation in Poisson regression with random coecients, Journal of Statistical Planning and Inference, 143, 296- 306, 2013.
  • Pinheiro, J. C. and Bates D. M., Mixed-Eects Models in S and S-Plus, (New York: Springer, 2000).
  • Pukelsheim, F. (1993). Optimal design of Experiments, (New York: Wiley, 1993).
  • Stufken, J. and Yang, M., On locally Optimal designs for Generalized Linear Models with group eects, The statistica sinica, 22, 1756-1786, 2012.
  • Waterhouse, T. H. (2005). Optimal Experimental Design for Nonlinear and Generalised Linear Models, (PhD thesis, University of Queensland, Australia, 2005).
Toplam 11 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Matematik
Bölüm İstatistik
Yazarlar

S. Mehr Mansour Bu kişi benim

Mehrdad Niaparast Bu kişi benim

Yayımlanma Tarihi 1 Nisan 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 47 Sayı: 2

Kaynak Göster

APA Mansour, S. M., & Niaparast, M. (2018). Efficiency of D-optimal designs for quasi-likelihood estimation in Poisson regression model with random effects. Hacettepe Journal of Mathematics and Statistics, 47(2), 463-470.
AMA Mansour SM, Niaparast M. Efficiency of D-optimal designs for quasi-likelihood estimation in Poisson regression model with random effects. Hacettepe Journal of Mathematics and Statistics. Nisan 2018;47(2):463-470.
Chicago Mansour, S. Mehr, ve Mehrdad Niaparast. “Efficiency of D-Optimal Designs for Quasi-Likelihood Estimation in Poisson Regression Model With Random Effects”. Hacettepe Journal of Mathematics and Statistics 47, sy. 2 (Nisan 2018): 463-70.
EndNote Mansour SM, Niaparast M (01 Nisan 2018) Efficiency of D-optimal designs for quasi-likelihood estimation in Poisson regression model with random effects. Hacettepe Journal of Mathematics and Statistics 47 2 463–470.
IEEE S. M. Mansour ve M. Niaparast, “Efficiency of D-optimal designs for quasi-likelihood estimation in Poisson regression model with random effects”, Hacettepe Journal of Mathematics and Statistics, c. 47, sy. 2, ss. 463–470, 2018.
ISNAD Mansour, S. Mehr - Niaparast, Mehrdad. “Efficiency of D-Optimal Designs for Quasi-Likelihood Estimation in Poisson Regression Model With Random Effects”. Hacettepe Journal of Mathematics and Statistics 47/2 (Nisan 2018), 463-470.
JAMA Mansour SM, Niaparast M. Efficiency of D-optimal designs for quasi-likelihood estimation in Poisson regression model with random effects. Hacettepe Journal of Mathematics and Statistics. 2018;47:463–470.
MLA Mansour, S. Mehr ve Mehrdad Niaparast. “Efficiency of D-Optimal Designs for Quasi-Likelihood Estimation in Poisson Regression Model With Random Effects”. Hacettepe Journal of Mathematics and Statistics, c. 47, sy. 2, 2018, ss. 463-70.
Vancouver Mansour SM, Niaparast M. Efficiency of D-optimal designs for quasi-likelihood estimation in Poisson regression model with random effects. Hacettepe Journal of Mathematics and Statistics. 2018;47(2):463-70.