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A Computationally Efficient Algorithm to Solve Generalized Method of Moments Estimating Equations Based on Secant Procedure
Naushad Ali Mamode Khan, M. Heenaye
Pages - 28 - 33     |    Revised - 01-07-2011     |    Published - 05-08-2011
Published in International Journal of Scientific and Statistical Computing (IJSSC)
Volume - 2   Issue - 1    |    Publication Date - July / August 2011  Table of Contents
MORE INFORMATION
References   |   Cited By (4)   |   Abstracting & Indexing
KEYWORDS
Newton-Raphson, Jacobian, Quadratic Inference Function
ABSTRACT
Generalized method of moment estimating function enables one to estimate regression parameters consistently and efficiently. However, it involves one major computational problem: in complex data settings, solving generalized method of moments estimating function via Newton-Raphson technique gives rise often to non-invertible Jacobian matrices. Thus, parameter estimation becomes unreliable and computationally inefficient. To overcome this problem, we propose to use secant method based on vector divisions instead of the usual Newton-Raphson technique to estimate the regression parameters. This new method of estimation demonstrates a decrease in the number of non-convergence iterations as compared to the Newton-Raphson technique and provides reliable estimates.
CITED BY (4)  
1 Makkar, S. R., Williamson, A., Turner, T., Redman, S., & Louviere, J. (2015). Using conjoint analysis to develop a system to score research engagement actions by health decision makers. Health Research Policy and Systems, 13(1), 22.
2 Zhaoming Tao, & Xu Xiaoli. (2014). Quadratic function of longitudinal data to infer semi parametric model estimates. Statistics and Decision, (7), 8-10.
3 Zhaoming Tao, & Xu Xiaoli. (2014). Penalty semiparametric longitudinal model of quadratic inference function estimation. Statistics and Information Forum, 29 (8), 3-8.
4 Zhaoming Tao, & Xiao group. (2013). Longitudinal data non-punitive model parameters correction quadratic inference function estimation. Mathematics in Practice and Theory, 5, 031.
ABSTRACTING & INDEXING
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
REFERENCES
Hansen, L. (1982) ‘Large sample properties of generalized method of moments estimators’. Econometrika 50, 1029-54.
Mamode Khan, N. (2011), ‘ Use of vector equations in solving quasi-likelihood equations for Poisson model’, Journal of Statistical computation and simulation, iFirst,1-6, ISSN 0094- 9655
McKenzie, E. (1986), ‘Autoregressive moving-average processes with negative binomial and geometric marginal distributions. Advanced Applied Probability, 18, 679-705.
Qu, A and Lindsay, B. (2003), ‘ Building adaptive estimating equations when inverse of covariance estimation is difficult’. Journal of Royal Statistical Society 65, 127-142.
Qu, A, Lindsay, B and Li, B. (2000), ‘ Improving generalized estimating equations and quadratic inference functions’. Biometrika 91(2), 447-59.
Yixun,S. (2008) ‘Using vector divisions in solving non-linear equations’. International Journal of Contemporary Math Sciences 3(16): 753-759.
MANUSCRIPT AUTHORS
Dr. Naushad Ali Mamode Khan
University of Mauritius - Mauritius
n.mamodekhan@uom.ac.mu
Dr. M. Heenaye
- Mauritius


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