  We consider the estimation of the affine parameter (and power-law exponent)
in the preferential attachment model with random initial degrees. We derive the
likelihood, and show that the maximum likelihood estimator (MLE) is
asymptotically normal and efficient. We also propose a quasi-maximum-likelihood
estimator (QMLE) to overcome the MLE's dependence on the history of the initial
degrees. To demonstrate the power of our idea, we present numerical
simulations.
