A simple proof of the existence of the best estimator in a quasilinear regression model

DOI10.1007/s10957-013-0434-1zbMath1298.90108OpenAlexW1977430345MaRDI QIDQ467426

Dragan Jukić

Publication date: 3 November 2014

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10957-013-0434-1

zbMATH Keywords

existence problem\(l_p\)-norm estimatorquasilinear regression

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Nonlinear programming (90C30)

Related Items (2)

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Cites Work

• Criteria for unconstrained global optimization
• On nonlinear weighted total least squares parameter estimation problem for the three-parameter Weibull density
• On the existence of the best discrete approximation in \(l_p\) norm by reciprocals of real polynomials
• Criteria for global minimum of sum of squares in nonlinear regression
• Nonlinear estimation
• A necessary and sufficient criteria for the existence of the least squares estimate for a 3-parametric exponential function.
• Least-squares fitting Gompertz curve
• On the existence of best Mitscherlich, Verhulst, and West growth curves for generalized least-squares regression
• TheL_p-norm estimation of the parameters for the Jelinski–Moranda model in software reliability
• Least‐squares problems for Michaelis–Menten kinetics
• Quasilinearized Regression
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