Fibonacci numbers, Lucas numbers and integrals of certain Gaussian processes

DOI10.1090/S0002-9939-96-03691-XzbMath0869.60009MaRDI QIDQ3837630

Ludwig Baringhaus

Publication date: 10 December 1996

Published in: Proceedings of the American Mathematical Society (Search for Journal in Brave)

zbMATH Keywords

cumulantsGaussian processesintegral equationsFibonacci numbersLucas numbersgoodness of fittesting for symmetryempirical Fourier transformtesting for normalitydistributions of integrals of Gaussian processes

Mathematics Subject Classification ID

Probability distributions: general theory (60E05) Fibonacci and Lucas numbers and polynomials and generalizations (11B39)

Related Items (9)

Comments on: ``Tests for multivariate normality -- a critical review with emphasis on weighted \(L^2\)-statistics ⋮ On energy tests of normality ⋮ Goodness-of-fit tests for semiparametric and parametric hypotheses based on the probability weighted empirical characteristic function ⋮ On the eigenvalues associated with the limit null distribution of the Epps-Pulley test of normality ⋮ Bahadur efficiency for certain goodness-of-fit tests based on the empirical characteristic function ⋮ Tests of fit for normal inverse Gaussian distributions ⋮ Goodness-of-fit tests for multivariate Laplace distributions ⋮ On some characterizations and multidimensional criteria for testing homogeneity, symmetry and independence ⋮ Goodness-of-fit testing by transforming to normality: comparison between classical and characteristic function-based methods

Cites Work

• A consistent test for multivariate normality based on the empirical characteristic function
• The empirical characteristic function and its applications
• Multivariate empirical characteristic functions
• Weak Convergence of Probability Measures on the Function Space $C\lbrack 0, \infty)$
• The Cramer-Smirnov Test in the Parametric Case
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