Weak consistency of least-squares estimators in linear models

DOI10.1016/0047-259X(82)90014-8zbMath0483.62059MaRDI QIDQ1163323

D. Kaffes, M. Bhaskara Rao

Publication date: 1982

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

zbMATH Keywords

rates of convergenceconvergence in probabilityestimable linear functions

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Linear regression; mixed models (62J05) Central limit and other weak theorems (60F05)

Related Items (8)

On Complete Convergence for Weighted Sums of NA Random Fields ⋮ On complete convergence for weighted sums of asymptotically linear negatively dependent random field ⋮ A remark on the law of the logarithm for weighted sums of random variables with multidimensional indices ⋮ Complete convergence for weighted sums of \(\rho^*\)-mixing random fields ⋮ On complete convergence for weighted sums of martingale-difference random fields ⋮ A central limit theorem for weighted sums of associated random field ⋮ On weak consistency in linear models with equi-correlated random errors ⋮ Some limit theorems for weighted sums of random variable fields

Cites Work

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• Weak and strong consistency of the least squares estimators in regression models
• Rectangular Confidence Regions for the Means of Multivariate Normal Distributions
• Some More Results on Rates of Convergence in the Law of Large Numbers for Weighted Sums of Independent Random Variables
• Contributions to the Theory of Generalized Inverses

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