The linear regression model: Lpnorm estimation and the choice of p

DOI10.1080/03610918208812247zbMath0495.62070OpenAlexW2039482527MaRDI QIDQ3959308

Arthur H. Money, J. F. Affleck-Graves, Graham D. I. Barr, Michael L. Hart

Publication date: 1982

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918208812247

zbMATH Keywords

least squaresrelative efficienciesLp norm estimatesgeneralised varianceskurtosis of error distribution

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Monte Carlo methods (65C05)

Related Items (12)

Bagging binary and quantile predictors for time series ⋮ A generalized error distribution copula-based method for portfolios risk assessment ⋮ Optimal forecasting accuracy using Lp-norm combination ⋮ L₁for the simple linear regression model ⋮ Subbotin graphical models for extreme value dependencies with applications to functional neuronal connectivity ⋮ On optimal universal first-order methods for minimizing heterogeneous sums ⋮ A piecewise linear approximation procedure forL_pnorm curve fitting ⋮ Unnamed Item ⋮ Multivariate data modelling by metric approximants ⋮ Sparse regression for extreme values ⋮ Nonlinear L_P-norm estimation: part I - on the choice of the exponent, p, where the errors are additive ⋮ Nonlinear L_p-norm estimation: Part II: The asymptotic distribution gf the exponent, p, as a function of the sample kurtosis.

Cites Work

• On L1 and Chebyshev estimation
• A Note on the Generation of Random Normal Deviates
• Nonuniqueness of least absolute values regression
• On least absolute values estimation
• A Rapidly Convergent Descent Method for Minimization
• Uniform Random Number Generators

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