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The paper is devoted to the supremum-type multivariate goodness-of-fit tests based on the empirical characteristic function. Particular attention is devoted to the composite hypothesis of normality and Gaussian distribution mixture model. An analytical way to approximate the null asymptotic distributions of the considered test statistics is discussed applying the theory of large excursions of differentiable Gaussian random fields. The produced comparative Monte Carlo power study shows that the considered tests are powerful competitors to the existing classical criteria, clearly dominating in verification of the goodness-of-fit hypotheses against the specific types of alternatives. [ABSTRACT FROM AUTHOR]

Let { ξ( t) , t ∈ T} be a differentiable (in the mean-square sense) Gaussian random field with E ξ( t) ≡ 0, D ξ( t) ≡ 1, and continuous trajectories defined on the m-dimensional interval $ T \subset {\mathbb{R}^m} $. The paper is devoted to the problem of large excursions of the random field ξ. In particular, the asymptotic properties of the probability P = P{ −v( t) < ξ( t) < u( t) , t ∈ T}, when, for all t ∈ T, u( t) , v( t) ⩾ χ, χ → ∞, are investigated. The work is a continuation of Rudzkis research started in [R. Rudzkis, Probabilities of large excursions of empirical processes and fields, Sov. Math., Dokl., 45(1):226-228, 1992]. It is shown that if the random field ξ satisfies certain smoothness and regularity conditions, then P = e + Qo(1), where Q is a certain constructive functional depending on u, v, T, and the matrix function R( t) = cov( ξ′( t) , ξ′( t)). [ABSTRACT FROM AUTHOR]