TY  - JOUR
T1  - On the Equivalence of Two Quasi-Newton Schemes in Generalized Linear Models
AU - , Mbe Egom Nja 
JO  - Journal of Modern Mathematics and Statistics
VL  - 3
IS  - 1
SP  - 25
EP  - 28
PY  - 2009
DA  - 2001/08/19
SN  - 1994-5388
DO  - jmmstat.2009.25.28
UR  - https://makhillpublications.co/view-article.php?doi=jmmstat.2009.25.28
KW  - Fisher’s information
KW  -Gradient vector
KW  -Hessian matrix
KW  -likelihood function
KW  -quasi-likelihood function
KW  -weight function
AB  - The Iterative Weighted Least Squares and the Fisher’s Scoring methods are two most commonly used iterative maximum likelihood optimization methods in generalized linear models. The Fisher’s Scoring method is given in terms of the gradient vector. While, the Iterative Weighted Least Squares method is based on the adjusted dependent vector. Using the relation between the expected Hessian matrix and weighted sum of squares, established for quasi-likelihood function and the link between the expected Hessian and the weighted sum of cross product, a proof of the theorem on the equivalence of the two quasi-Newton schemes is presented.
ER  -