Selection tests for possibly misspecified hierarchical multinomial marginal models
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Summary
As the richness of the family of hierarchical marginal models leads to comparing models that do not satisfy a nesting relationship, statistical tests for model selection from non-nested, possibly misspecified marginal models are introduced.
- Type
- article
- Published
- 2019-07-24
- Cited by
- 2
- References
- 34
- OpenAlex
- https://openalex.org/W2964146800
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:199672205
Keywords
Marginal model, Categorical variable, Marginal distribution, Econometrics, Model selection
References
- Pairwise Likelihood Ratio Tests and Model Selection Criteria for Structural Equation Models with Ordinal Variables
- Essential Statistical Inference: Theory and Methods
- Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
- Testing non-nested structural equation models
- AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS
- On Information and Sufficiency
- Parameterizations and Fitting of Bi‐directed Graph Models to Categorical Data
- Computing the distribution of quadratic forms: Further comparisons between the Liu-Tang-Zhang approximation and exact methods
- Marginal Nested Interactions for Contingency Tables
- Distribution of a Sum of Weighted Chi-Square Variables
- Homogeneous Linear Predictor Models for Contingency Tables
- Computing the distribution of quadratic forms in normal variables
- Maximum Likelihood Estimation of Misspecified Models
- Making correct statistical inferences using a wrong probability model
- Marginal log-linear parameters for graphical Markov models
- Statistical Tests for Comparing Possibly Misspecified and Nonnested Models.
- Two algorithms for fitting constrained marginal models
- Identification in Parametric Models
- Marginal log-linear parameterization of conditional independence models
- Multinomial-Poisson models subject to inequality constraints
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