Functional central limit theorems for self-normalized least squares processes in regression with possibly infinite variance data

DOI10.1016/J.SPA.2011.07.012zbMath1274.62442OpenAlexW2042385457MaRDI QIDQ645604

Miklós Csörgő, Yuliya V. Martsynyuk

Publication date: 10 November 2011

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spa.2011.07.012

zbMATH Keywords

functional central limit theoreminfinite variancesignal-to-noise ratiodomain of attraction of the normal lawslowly varying function at infinitysimple linear regressionasymptotic confidence intervalCholesky square root of a matrixdirect product of two measurable spacesgeneralized domain of attraction of the \(d\)-variate normal lawnorm approximation in probabilitystandard/bivariate Wiener processstudentized/self-normalized least squares estimator/processsupsymmetric positive definite square root of a matrixuniform Euclidean norm approximation in probability

Mathematics Subject Classification ID

Infinitely divisible distributions; stable distributions (60E07) Linear regression; mixed models (62J05) Functional limit theorems; invariance principles (60F17)

Related Items (2)

Invariance principles for a multivariate Student process in the generalized domain of attraction of the multivariate normal law ⋮ Two-dimensional anisotropic random walks: fixed versus random column configurations for transport phenomena

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