On Quasi Likelihood Equations with Non‐parametric Weights

To apply the quasi likelihood method one needs both the mean and the variance functions to determine its optimal weights. If the variance function is unknown, then the weights should be acquired from the data. One way to do so is by adaptive estimation, which involves non‐parametric estimation of the variance function. Adaptation, however, also brings in noise that hampers its improvement for moderate samples. In this paper we introduce an alternative method based not on the estimation of the variance function, but on the penalized minimization of the asymptotic variance of the estimator. By doing so we are able to retain a restricted optimality under the smoothness condition, however strong that condition may be. This is important because for moderate sample sizes we need to impose a strong smoothness constraint to damp the noise—often stronger than would be adequate for the adaptive method. We will give a rigorous development of the related asymptotic theory, and provide the simulation evidence for the advantage of this method.