Robust designs for multinomial models

DOI10.1080/03610918.2018.1473588OpenAlexW2914599794WikidataQ128591062 ScholiaQ128591062MaRDI QIDQ5087972

Ishapathik Das, Siuli Mukhopadhyay

Publication date: 4 July 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2018.1473588

zbMATH Keywords

quantile dispersion graphsfamily of link functionsmultivariate krigingmultivariate logistic link

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• Matlab
• Fahrmeir
• MCS

Cites Work

• On generalized multinomial models and joint percentile estimation
• Robust designs for misspecified logistic models
• Robust designs for generalized linear models with possible overdispersion and misspecified link functions
• The effect of link misspecification on binary regression inference
• Global optimization by multilevel coordinate search
• On selecting parametric link transformation families in generalized linear models
• Bayesian ordinal and binary regression models with a parametric family of mixture links
• Comparison of designs for generalized linear models under model misspecification
• Comparison of designs for multivariate generalized linear models
• Goodness of Link Tests for Generalized Linear Models
• On two families of transformations to additivity for binary response data
• Discrimination among some parametric models
• Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large
• Robust Designs in Generalized Linear Models: A Quantile Dispersion Graphs Approach
• Multivariate statistical modelling based on generalized linear models.

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