Central limit theorems in linear structural error-in-variables models with explanatory variables in the domain of attraction of the normal law

DOI10.1214/07-EJS056zbMath1320.62182arXiv0706.0826MaRDI QIDQ2426800

Yuliya V. Martsynyuk

Publication date: 14 May 2008

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0706.0826

zbMATH Keywords

central limit theoremdomain of attractionnormal lawmeasurement errorsself-normalizationexplanatory variablesreliability ratioStudentizationmodified least squares estimatorslarge-sample approximate confidence intervallinear structural error-in-variables model

Mathematics Subject Classification ID

Infinitely divisible distributions; stable distributions (60E07) Central limit and other weak theorems (60F05) Linear inference, regression (62J99) Nonparametric tolerance and confidence regions (62G15)

Related Items (6)

Nonparametric detection of change in the slope and intercept in linear structural errors-in-variables models ⋮ Exploring functional CLT confidence intervals for a population mean in the domain of attraction of the normal law ⋮ On consistency of the least squares estimators in linear errors-in-variables models with infinite variance errors ⋮ Estimation for a longitudinal linear model with measurement errors ⋮ Functional central limit theorems for self-normalized least squares processes in regression with possibly infinite variance data ⋮ Functional asymptotic confidence intervals for the slope in linear error-in-variables models

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