This research creates a general class of "perturbation models" which are described by an underlying "null" model that accounts for most of the structure in data and a perturbation that accounts for possible small localized departures. The perturbation models encompass finite mixture models and spatial scan process. In this article, (1) we propose a new test statistic to detect the presence of perturbation, including the case where the null model contains a set of nuisance parameters, and show that it is equivalent to the likelihood ratio test; (2) we establish that the asymptotic distribution of the test statistic is equivalent to the supremum of a Gaussian random field over a high-dimensional manifold (e.g., curve, surface etc.) with boundaries and singularities; (3) we derive a technique for approximating the quantiles of the test statistic using the Hotelling-Weyl-Naiman "volume-of-tube formula"; and (4) we solve the long-pending problem of testing for the order of a mixture model; in particular, derive the asymptotic null distribution for a general family of mixture models including the multivariate mixtures. The inferential theory developed in this article is applicable for a class of non-regular statistical problems involving loss of identifiability or when some of the parameters are on the boundary of the parametric space.