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Marginal models for categorical data
Statistical models defined by imposing restrictions on marginal distributions of contingency tables have received considerable attention recently. This paper introduces a general definition ofExpand
A consistent test of independence based on a sign covariance related to Kendall's tau
The most popular ways to test for independence of two ordinal random variables are by means of Kendall's tau and Spearman's rho. However, such tests are not consistent, only having power forExpand
Marginal models for dependent, clustered, and longitudinal categorical data
Loglinear Marginal Models.- Nonloglinear Marginal Models.- Marginal Analysis of Longitudinal Data.- Causal Analyses: Structural Equation Models and (Quasi-)Experimental Designs.- Marginal modelingExpand
Nonparametric Testing of Conditional Independence by Means of the Partial Copula
We propose a new method to test conditional independence of two real random variables $Y$ and $Z$ conditionally on an arbitrary third random variable $X$. The partial copula is introduced, defined asExpand
Testing conditional independence for continuous random variables
A common statistical problem is the testing of independence of two (response) variables conditionally on a third (control) variable. In the first part of this paper, we extend Hoeding’s concept ofExpand
Polytomous Latent Scales for the Investigation of the Ordering of Items
We propose three latent scales within the framework of nonparametric item response theory for polytomously scored items. Latent scales are models that imply an invariant item ordering, meaning thatExpand
A Kernel Test for Three-Variable Interactions
TLDR
We introduce kernel nonparametric tests for Lancaster three-variable interaction and for total independence, using embeddings of signed measures into a reproducing kernel Hilbert space. Expand
Bayesian Posterior Estimation of Logit Parameters with Small Samples
When the sample size is small compared to the number of cells in a contingency table, maximum likelihood estimates of logit parameters and their associated standard errors may not exist or may beExpand
Parameterizations and Fitting of Bi-directed Graph Models to Categorical Data
We discuss two parameterizations of models for marginal independencies for discrete distributions which are representable by bi-directed graph models, under the global Markov property. Such modelsExpand
On applications of marginal models for categorical data
Summary - The paper considers marginal models for categorical data and after reviewing the most important theoretical results concerning the definition, estimation and testing of such models,Expand
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