Locally \(D\)-optimal designs for heteroscedastic polynomial measurement error models
From MaRDI portal
Publication:2189755
DOI10.1007/s00184-019-00745-2zbMath1445.62205OpenAlexW2973091665WikidataQ127284163 ScholiaQ127284163MaRDI QIDQ2189755
Publication date: 16 June 2020
Published in: Metrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00184-019-00745-2
heteroscedasticitymeasurement error modelapproximate design theoryChebycheff systemcorrected score function approachlocal \(D\)-optimality
Related Items (1)
Cites Work
- D-optimum designs in multi-factor models with heteroscedastic errors
- General equivalence theory for optimum designs (approximate theory)
- \(G\)-optimal designs for multi-factor experiments with heteroscedastic errors
- R-optimal designs for multi-factor models with heteroscedastic errors
- Information matrices with random regressors. Application to experimental design
- Design of experiments in the presence of errors in factor levels
- On the equivalence of D and G-optimal designs in heteroscedastic models
- Consistent estimation and testing in heteroscedastic polynomial errors-in-variables models
- A general approach to \(D\)-optimal designs for weighted univariate polynomial regression models
- A-optimal designs for heteroscedastic multifactor regression models
- Corrected score function for errors-in-variables models: Methodology and application to generalized linear models
- Locally optimal designs for errors-in-variables models
- Unbiased estimation of a nonlinear function a normal mean with application to measurement err oorf models
- CORRECTED SCORE FUNCTIONS IN CLASSICAL ERROR‐IN‐VARIABLES AND INCIDENTAL PARAMETER MODELS
- Bayesian D‐optimal designs for error‐in‐variables models
- Optimal Designs for Quantile Regression Models
- Optimal Design of Experiments
- Measurement Error in Nonlinear Models
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Locally \(D\)-optimal designs for heteroscedastic polynomial measurement error models