Functional asymptotic confidence intervals for the slope in linear error-in-variables models

DOI10.1007/s10474-008-8075-9zbMath1179.62044OpenAlexW2118935708MaRDI QIDQ1046876

Yuliya V. Martsynyuk

Publication date: 29 December 2009

Published in: Acta Mathematica Hungarica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10474-008-8075-9

zbMATH Keywords

functional central limit theoremWiener processdomain of attraction of the normal lawreliability ratioGleser-Hwang effect(self-randomized) modified least squares processfunctional asymptotic confidence intervallinear structural/functional error-in-variables modelsup-norm approximation in probability

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Parametric tolerance and confidence regions (62F25) Linear regression; mixed models (62J05) Functional limit theorems; invariance principles (60F17)

Related Items (5)

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 ⋮ Functional asymptotic confidence intervals for a common mean of independent random variables ⋮ Functional central limit theorems for self-normalized least squares processes in regression with possibly infinite variance data ⋮ Weak Convergence of Self-normalized Partial Sums Processes

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The nonexistence of \(100(1-\alpha)\%\) confidence sets of finite expected diameter in errors-in-variables and related models
• Quadratic negligibility and the asymptotic normality of operator normed sums
• Donsker's theorem for self-normalized partial sums processes
• Invariance principles for adaptive self-normalized partial sums processes.
• Central limit theorems in linear structural error-in-variables models with explanatory variables in the domain of attraction of the normal law
• New multivariate central limit theorems in linear structural and functional error-in-variables models
• A limit theorem for sample maxima and heavy branches in Galton–Watson trees
• A Theorem on Products of Random Variables, With Application to Regression
• A glimpse of the impact of pál erdős on probability and statistics

This page was built for publication: Functional asymptotic confidence intervals for the slope in linear error-in-variables models