A pool-adjacent-violators-algorithm approach to detect infinite parameter estimates in one-regressor dose–response models with asymptotes

DOI10.1080/00949655.2013.793344zbMath1453.62721OpenAlexW2018182378WikidataQ58071694 ScholiaQ58071694MaRDI QIDQ5219511

Roland C. Deutsch, Brian Habing, Walter W. Piegorsch

Publication date: 12 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2013.793344

zbMATH Keywords

maximum likelihoodinfinite estimatesAbbott adjustmentdose-response modellingPAV-algorithmseparated data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10)

Uses Software

• R

Cites Work

• Simultaneous confidence bands for Abbott-adjusted quantal response models in benchmark analysis
• Maximum likelihood estimates in exponential response models
• Constrained Statistical Inference
• An Empirical Distribution Function for Sampling with Incomplete Information
• On the existence of maximum likelihood estimates in logistic regression models
• A note on A. Albert and J. A. Anderson's conditions for the existence of maximum likelihood estimates in logistic regression models
• On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models
• Infinite Parameter Estimates in Logistic Regression, with Application to Approximate Conditional Inference
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