A non-iterative procedure for maximum likelihood estimation of the parameters of Mallows' model based on partial rankings

The problem of maximum likelihood estimation of the parameters of Mallows' ranking model based on partial rankings (with a fixed number of tie groups) has been approached by Beckett (1992), applying the EM algorithm to estimate both the center and the scale parameter. This paper offers an alternative procedure for maximum likelihood estimation without relying an the EM algorithm: the center is estimated by minimizing the sum of the distances between the center and the observed partial rankings, and the scale parameter is estimated by solving a relatively simple equation.