Strong consistency of least squares estimates with i.i.d. errors with mean values not necessarily defined

DOI10.1080/02331888.2011.581759zbMath1440.62274OpenAlexW2083532227MaRDI QIDQ2863065

João Tiago Mexia, João Lita da Silva

Publication date: 21 November 2013

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2011.581759

zbMATH Keywords

regression modelsstrong consistencyleast squares estimatesMarcinkiewicz-Zygmund lawundefined errors mean values

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Strong limit theorems (60F15)

Related Items (8)

On the rates of convergence for moments convergence in regression models ⋮ Almost sure convergence for weighted sums of φ-mixing random variables with applications ⋮ Complete convergence and complete moment convergence for maximal weighted sums of arrays of rowwise extended negatively dependent random variables with statistical applications ⋮ On a class of linear regression methods ⋮ Strong consistency of least squares estimates in multiple regression models with random regressors ⋮ On sufficient conditions for the strong consistency of least-squares estimates ⋮ Some strong consistency results in stochastic regression ⋮ On the Strong Consistency of Ridge Estimates

Cites Work

• Strong consistency of least squares estimates in multiple regression II
• Extension of Lai-Robbins-Wei's theorem
• Convergence systems and strong consistency of least squares estimates in regression models
• Matrix Analysis
• Weak and strong consistency of the least squares estimators in regression models
• Unnamed Item

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