ArticleJournal of econometrics2020
A Projection-based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models.
Article in Journal of econometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A mutual information criterion with applications to canonical correlation analysis and graphical models.Stat (International Statistical Institute) · 2021Article
- Brain Imaging Genomics: Integrated Analysis and Machine Learning.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Fast and Accurate Detection of Complex Imaging Genetics Associations Based on Greedy Projected Distance Correlation.IEEE transactions on medical imaging · 2018Article
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Authors and funding
3 authors.
Funding
Abstract
Measuring conditional dependence is an important topic in econometrics with broad applications including graphical models. Under a factor model setting, a new conditional dependence measure based on projection is proposed. The corresponding conditional independence test is developed with the asymptotic null distribution unveiled where the number of factors could be high-dimensional. It is also shown that the new test has control over the asymptotic type I error and can be calculated efficiently. A generic method for building dependency graphs without Gaussian assumption using the new test is elaborated. We show the superiority of the new method, implemented in the R package pgraph, through simulation and real data studies.
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Registered trials
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