THE QUASI-LIKELIHOOD ESTIMATION IN REGRESSION

I propose a simply method to estimate the regression parameters in qua si-likelihood model. My main approach utilizes the dimension reduction technique to first reduce the dimension of the regressor X to one dim ension before solving the quasi-likelihood equations. In addition, the real advantage of using dimension reduction technique is that it prov ides a good initial estimate for one-step estimator of the regression parameters. Under certain design conditions, the estimators are asympt otically multivariate normal and consistent. Moreover, a Monte Carlo s imulation is used to study the practical performance of the procedures , and I also assess the cost of CPU time for computing the estimates.