
Interaction Models and Generalized Score Matching for Compositional Data
Applications such as the analysis of microbiome data have led to renewed...
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On AzadkiaChatterjee's conditional dependence coefficient
In recent work, Azadkia and Chatterjee laid out an ingenious approach to...
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Causal Structural Learning Via Local Graphs
We consider the problem of learning causal structures in sparse highdim...
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Confidence in Causal Discovery with Linear Causal Models
Structural causal models postulate noisy functional relations among a se...
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Definite NonAncestral Relations and Structure Learning
In causal graphical models based on directed acyclic graphs (DAGs), dire...
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Generalized Score Matching for General Domains
Estimation of density functions supported on general domains arises when...
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On the power of Chatterjee rank correlation
Chatterjee (2020) introduced a simple new rank correlation coefficient t...
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Causal Discovery with Unobserved Confounding and nonGaussian Data
We consider the problem of recovering causal structure from multivariate...
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Rateoptimality of consistent distributionfree tests of independence based on centeroutward ranks and signs
Rank correlations have found many innovative applications in the last de...
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Structure Learning for Cyclic Linear Causal Models
We consider the problem of structure learning for linear causal models b...
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Directed Graphical Models and Causal Discovery for ZeroInflated Data
Modern RNA sequencing technologies provide gene expression measurements ...
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Existence and Uniqueness of the Kronecker Covariance MLE
In matrixvalued datasets the sampled matrices often exhibit correlation...
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Statistical significance in highdimensional linear mixed models
This paper concerns the development of an inferential framework for high...
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Distributionfree consistent independence tests via Hallin's multivariate rank
This paper investigates the problem of testing independence of two rando...
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Generalized Score Matching for NonNegative Data
A common challenge in estimating parameters of probability density funct...
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High dimensional independence testing with maxima of rank correlations
Testing mutual independence for high dimensional observations is a funda...
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Algebraic tests of general Gaussian latent tree models
We consider general Gaussian latent tree models in which the observed va...
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Nested Covariance Determinants and Restricted Trek Separation in Gaussian Graphical Models
Directed graphical models specify noisy functional relationships among a...
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On Causal Discovery with Equal Variance Assumption
Prior work has shown that causal structure can be uniquely identified fr...
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The Maximum Likelihood Threshold of a Path Diagram
Linear structural equation models postulate noisy linear relationships b...
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HighDimensional Causal Discovery Under nonGaussianity
We consider data from graphical models based on a recursive system of li...
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HighDimensional Discovery Under nonGaussianity
We consider data from graphical models based on a recursive system of li...
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Graphical Models for NonNegative Data Using Generalized Score Matching
A common challenge in estimating parameters of probability density funct...
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Robust and sparse Gaussian graphical modeling under cellwise contamination
Graphical modeling explores dependences among a collection of variables ...
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Empirical Likelihood for Linear Structural Equation Models with Dependent Errors
We consider linear structural equation models that are associated with m...
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Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence
The need to test whether two random vectors are independent has spawned ...
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Structure Learning in Graphical Modeling
A graphical model is a statistical model that is associated to a graph w...
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Marginal likelihood and model selection for Gaussian latent tree and forest models
Gaussian latent tree models, or more generally, Gaussian latent forest m...
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Orderinvariant prior specification in Bayesian factor analysis
In (exploratory) factor analysis, the loading matrix is identified only ...
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Robust Graphical Modeling with tDistributions
Graphical Gaussian models have proven to be useful tools for exploring n...
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Nonparametric Reduced Rank Regression
We propose an approach to multivariate nonparametric regression that gen...
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A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence
Graphical models with bidirected edges (<>) represent marginal indepen...
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Iterative Conditional Fitting for Gaussian Ancestral Graph Models
Ancestral graph models, introduced by Richardson and Spirtes (2002), gen...
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Exact blockwise optimization in group lasso and sparse group lasso for linear regression
The group lasso is a penalized regression method, used in regression pro...
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Robust graphical modeling of gene networks using classical and alternative Tdistributions
Graphical Gaussian models have proven to be useful tools for exploring n...
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