Consistency for least squares regression estimators with infinite variance data

DOI10.1016/0378-3758(89)90087-6zbMath0679.62054OpenAlexW2062854175MaRDI QIDQ1822869

Daren B. H. Cline

Publication date: 1989

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0378-3758(89)90087-6

zbMATH Keywords

regular variationweak consistencyinfinite variancemultiple regressionleast squares estimatorsstable domain of attractionrandom predictors

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Asymptotic properties of parametric tests (62F05)

Related Items (8)

Optimal kernel estimation of densities ⋮ Stable Autoregressive Models and Signal Estimation ⋮ On consistency of the least squares estimators in linear errors-in-variables models with infinite variance errors ⋮ Functional central limit theorems for self-normalized least squares processes in regression with possibly infinite variance data ⋮ Abelian and Tauberian theorems relating the local behavior of an integrable function to the tail behavior of its Fourier transform ⋮ Testing for (in)finite moments ⋮ Least tail-trimmed squares for infinite variance autoregressions ⋮ Subexponentiality of the product of independent random variables

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