Parameter Estimation for Models with Unknown Parameters in Variance

DOI10.1081/STA-200037917zbMath1087.62065OpenAlexW2071670471MaRDI QIDQ5290374

Sergei L. Leonov, Valerii V. Fedorov

Publication date: 28 April 2006

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

Full work available at URL: https://doi.org/10.1081/sta-200037917

zbMATH Keywords

Maximum likelihoodIteratively reweighted estimatorsMultiple responsesUnknown parameters in variance

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) General nonlinear regression (62J02)

Related Items

Elemental information matrices and optimal experimental design for generalized regression models

Cites Work

• Asymptotic theory of nonlinear least squares estimation
• Quasi-likelihood and its application. A general approach to optimal parameter estimation
• Unbalanced Repeated-Measures Models with Structured Covariance Matrices
• Variance Function Estimation
• Heteroscedastic Nonlinear Regression
• Least Squares Estimation when the Covariance Matrix and Parameter Vector are Functionally Related
• MODELING AND ESTIMATING VARIANCES IN REGRESSION
• Regression problems with controllable variables subject to error
• Design of experiments with unknown parameters in variance
• Generalized Estimating Equations
• Asymptotic Properties of Non-Linear Least Squares Estimators
• Linear Statistical Inference and its Applications
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