On existence and uniqueness of maximum likelihood estimates in quantal and ordinal response models

DOI10.1007/BF02613318zbMath0671.62053MaRDI QIDQ1119314

S. H. Smith

Publication date: 1988

Published in: Metrika (Search for Journal in Brave)

Full work available at URL: https://eudml.org/doc/176193

zbMATH Keywords

affineconcave functionlogit modelidentifiablebinomial response modelscell countscumulative and sequential modelsdirection setsexistence and uniqueness of maximum likelihood estimatesextension of the maximum likelihood principlelinear probability modellog likelihoodquantal and ordinal response modelsuniquely estimable linear functions

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12)

Related Items (8)

Sequential models in categorical regression ⋮ Maximum likelihood estimation in misspecified generalized linear models ⋮ A comparison of optimization solvers for log binomial regression including conic programming ⋮ On binary and categorical time series models with feedback ⋮ On directions of strictness, affinity and constancy and the minimum set of a proper convex function ⋮ On existence and uniqueness of a vector minimizing a convex function ⋮ The asymptotic distribution of the proportion of correct classifications for a holdout sample in logistic regression ⋮ Prediction and classification of non-stationary categorical time series

Cites Work

• On the existence and uniqueness of the maximum likelihood estimate of a vector-valued parameter in fixed-size samples
• On the existence of maximum likelihood estimates in logistic regression models
• On directions of strictness, affinity and constancy and the minimum set of a proper convex function
• A note on A. Albert and J. A. Anderson's conditions for the existence of maximum likelihood estimates in logistic regression models
• On existence and uniqueness of a vector minimizing a convex function
• Concavity of the Log Likelihood
• On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models
• REGRESSION MODELS FOR NON‐STATIONARY CATEGORICAL TIME SERIES
• Convex Analysis
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