The Asymptotic Covariance Matrix of the Least Squares Estimator in the Stochastic Linear Regression Model: The Case of Elliptically Symmetric Distribution

DOI10.1080/03610920802278866zbMath1159.62042OpenAlexW2027881613MaRDI QIDQ3622079

Jiro Hodoshima

Publication date: 23 April 2009

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920802278866

zbMATH Keywords

non-normalityasymptotic robustnessunconditional inference

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Cites Work

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• On adaptive estimation
• A characterization of the distributions that imply mean-variance utility functions
• Mardia's coefficient of kurtosis in elliptical populations
• Tail-thickness in terms of COV(\(X_{j}^{2}\),\(X_{p}^{2}\)) in the class of elliptical distributions.
• Family of multivariate generalized \(t\) distributions
• On theory testing in econometrics. Modeling with nonexperimental data
• A Tale of Two Regressions
• Statistical Adequacy and the Testing of Trend Versus Difference Stationarity
• Common risk factors in the returns on stocks and bonds
• Measures of multivariate skewness and kurtosis with applications
• Linear Statistical Inference and its Applications

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