Robust designs for misspecified logistic models
From MaRDI portal
Publication:953833
DOI10.1016/j.jspi.2008.05.022zbMath1154.62059OpenAlexW2038336950MaRDI QIDQ953833
Adeniyi J. Adewale, Douglas P. Wiens
Publication date: 6 November 2008
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jspi.2008.05.022
simulated annealingFisher informationpolynomiallogistic regressionrandom walklinear predictorMonte Carlo sample
Optimal statistical designs (62K05) Generalized linear models (logistic models) (62J12) Monte Carlo methods (65C05) Robust parameter designs (62K25)
Related Items (11)
Comparison of designs for generalized linear models under model misspecification ⋮ Robust designs for dose-response studies: model and labelling robustness ⋮ Robust and efficient estimation of effective dose ⋮ Robust designs for multinomial models ⋮ On generalized multinomial models and joint percentile estimation ⋮ Robust designs for generalized linear mixed models with possible model misspecification ⋮ A model robust subsampling approach for generalised linear models in big data settings ⋮ Robustness of design for the testing of lack of fit and for estimation in binary response models ⋮ Robust designs for generalized linear models with possible overdispersion and misspecified link functions ⋮ New criteria for robust integer-valued designs in linear models ⋮ Robust Designs in Generalized Linear Models: A Quantile Dispersion Graphs Approach
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- New criteria for robust integer-valued designs in linear models
- Optimal Bayesian design applied to logistic regression experiments
- Robust designs for nearly linear regression
- Minimax designs for approximately linear regression
- Optimal Bayesian designs for models with partially specified heteroscedastic structure
- Maximum likelihood estimation in misspecified generalized linear models
- Recent Advances in Nonlinear Experimental Design
- Constructing Exact D-Optimal Experimental Designs by Simulated Annealing
- A sequentially constructed design for estimating a nonlinear parametric function
- Experimental Design for Binary Data
- Experimental design in a class of models
- Optimal multipurpose designs for regression models
- D-optimal designs for generalised linear models with variance proportional to the square of the mean
- Minimax designs for approximately linear models with AR(1) errors
- Integer-Valued, Minimax Robust Designs for Estimation and Extrapolation in Heteroscedastic, Approximately Linear Models
- Minimax D‐Optimal Designs for the Logistic Model
- Robust sequential designs for nonlinear regression
- Nonlinear Experiments: Optimal Design and Inference Based on Likelihood
- Bayesian and maximin optimal designs for heteroscedastic regression models
- A Basis for the Selection of a Response Surface Design
- Locally Optimal Designs for Estimating Parameters
- Maximum Likelihood Estimation of Misspecified Models
This page was built for publication: Robust designs for misspecified logistic models