Least Squares Estimation when the Covariance Matrix and Parameter Vector are Functionally Related
From MaRDI portal
Publication:3878570
DOI10.2307/2287408zbMath0437.62064OpenAlexW4237446900MaRDI QIDQ3878570
Publication date: 1980
Full work available at URL: https://doi.org/10.2307/2287408
asymptotic propertieslinear modelmaximum likelihood estimatortwo-step estimationestimated covariance matrixweighted joint least squares estimator
Related Items (25)
Parameter Estimation for Models with Unknown Parameters in Variance ⋮ Optimal designs based on the maximum quasi-likelihood estimator ⋮ Estimation of the parameters of a time series subject to the error of rotation sampling ⋮ Iterated weighted least squares in heteroscedastic lineaipmod%81è ⋮ Nonlinear least squares and maximum likelihood estimation of a heteroscedastic regression model ⋮ Parameter estimation in a two-compartment population pharmacokinetic model with destructive sampling ⋮ A note on millers's empirical weights for heteroscedastic linear regression ⋮ The collected works of john w. tukey ⋮ Test for the choice of approximative models in nonlinear regression when the variance is unknown ⋮ Optimal designs for some stochastic processes whose covariance is a function of the mean ⋮ On the asymptotics of distributions of two-step statistical estimates ⋮ A linear model-based test for the heterogeneity of conditional correlations ⋮ Estimation in one–way classification with heteroscedastic error ⋮ Generalized nonlinear models and variance function estimation ⋮ Compromise designs in heteroscedastic linear models ⋮ Improvement of estimators in a linear regression problem with random errors in coefficients ⋮ Optimal Designs When the Variance Is A Function of the Mean ⋮ Robust estimation and variable selection in heteroscedastic linear regression ⋮ Doubly penalized likelihood estimator in heteroscedastic regression ⋮ Bias-corrected heterosced asticity robust covariance matrix (sandwich) estimators ⋮ Robust Testing Procedures in Heteroscedastic Linear Models ⋮ Seemingly unrelated regressions under additive heteroscedasticity. Theory and share equation applications ⋮ Some results for robust GM-based estimators in heteroscedastic regression models ⋮ Robust estimation in certain heteroscedastic linear models when there are many parameters ⋮ A convergence theorem for sums of dependent Hilbert space valued triangular arrays
This page was built for publication: Least Squares Estimation when the Covariance Matrix and Parameter Vector are Functionally Related