1 - 9 of 9 articles
The R package
was recently released to implement generalized structured component analysis (GSCA). GSCA represents a component-based approach to structural equation modeling (SEM) that defines a latent variable as a component or weighted composite of indicators.
enables users to...
In multivariate multiple linear regression with a non-negative ridge parameter, when a model is underspecified, the asymptotic biases of the Mallows C
and its modifications are derived up to order O(1) under non-normality. For a not underspecified model, the asymptotic biases are of smaller...
A family of the estimators adjusting the maximum likelihood estimator by a higher-order term maximizing the asymptotic predictive expected log-likelihood is introduced under possible model misspecification. The negative predictive expected log-likelihood is seen as the Kullback–Leibler distance...
In Bayes score-based Bayesian network structure learning (BNSL), we are to specify two prior probabilities: over the structures and over the parameters. In this paper, we mainly consider the parameter priors, in particular for the BDeu (Bayesian Dirichlet equivalent uniform) and Jeffreys’ prior....
Some open theoretical questions are addressed on how the mind and brain represent and process concepts, particularly as they are instantiated in particular human languages. Recordings of neuroimaging data should provide a suitable empirical basis for investigating this topic, but the complexity...
Causal feature learning (CFL) (Chalupka et al., Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. AUAI Press, Edinburgh, pp 181–190, 2015) is a causal inference framework rooted in the language of causal graphical models (Pearl J, Reasoning and inference....
In this article, the most commonly used algorithms for causal search on fMRI data are reviewed and discussed, with particular attention paid to aspects of the algorithms useful for substantive neuroimaging researchers. Classic algorithms, such as PC and GES, as well as more contemporary...
Structural mean models (SMMs) have been proposed for estimating causal treatment effects in the presence of non-ignorable non-compliance in clinical trials. To obtain a valid causal estimate, we must impose several assumptions. One of these is the correct specification of the parametric part of...
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