  Score matching is an estimation procedure that has been developed for
statistical models whose probability density function is known up to
proportionality but whose normalizing constant is intractable, so that maximum
likelihood is difficult or impossible to implement. To date, applications of
score matching have focused more on continuous IID models. Motivated by various
data modelling problems, this article proposes a unified asymptotic theory of
generalized score matching developed under the independence assumption,
covering both continuous and discrete response data, thereby giving a sound
basis for score-matchingbased inference. Real data analyses and simulation
studies provide convincing evidence of strong practical performance of the
proposed methods.
