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  • Search: subject:"Sup–norm approximation in probability"
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Asymptotic confidence interval 1 Cholesky square root of a matrix 1 Direct product of two measurable spaces 1 Domain of attraction of the normal law 1 Functional central limit theorem 1 Generalized domain of attraction of the d-variate normal law 1 Infinite variance 1 Signal-to-noise ratio 1 Simple linear regression 1 Slowly varying function at infinity 1 Standard/bivariate Wiener process 1 Studentized/self-normalized least squares estimator/process 1 Sup–norm approximation in probability 1 Symmetric positive definite square root of a matrix 1 Uniform Euclidean norm approximation in probability 1
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Csörgő, Miklós 1 Martsynyuk, Yuliya V. 1
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Stochastic Processes and their Applications 1
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Functional central limit theorems for self-normalized least squares processes in regression with possibly infinite variance data
Csörgő, Miklós; Martsynyuk, Yuliya V. - In: Stochastic Processes and their Applications 121 (2011) 12, pp. 2925-2953
Based on an R2-valued random sample {(yi,xi),1≤i≤n} on the simple linear regression model yi=xiβ+α+εi with unknown error variables εi, least squares processes (LSPs) are introduced in D[0,1] for the unknown slope β and intercept α, as well as for the unknown β when α=0. These LSPs...
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