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Found 30 papers, 7 papers with code
Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model.
Learning Directed Acyclic Graphs from Partial Orderings
In general, learning the DAG structure is both computationally and statistically challenging.
A Penalized Poisson Likelihood Approach to High-Dimensional Semi-Parametric Inference for Doubly-Stochastic Point Processes
Doubly-stochastic point processes model the occurrence of events over a spatial domain as an inhomogeneous Poisson process conditioned on the realization of a random intensity function.
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm
To overcome these challenges, in this paper we propose an approximate EM algorithm for Markov-switching VAR models that leads to efficient computation and also facilitates the investigation of asymptotic properties of the resulting parameter estimates.
Joint Estimation and Inference for Multi-Experiment Networks of High-Dimensional Point Processes
Modern high-dimensional point process data, especially those from neuroscience experiments, often involve observations from multiple conditions and/or experiments.
Causal Discovery in High-Dimensional Point Process Networks with Hidden Nodes
Thanks to technological advances leading to near-continuous time observations, emerging multivariate point process data offer new opportunities for causal discovery.
Interaction Models and Generalized Score Matching for Compositional Data
Applications such as the analysis of microbiome data have led to renewed interest in statistical methods for compositional data, i. e., multivariate data in the form of probability vectors that contain relative proportions.
Definite Non-Ancestral Relations and Structure Learning
Ancestral relations between variables play an important role in causal modeling.
Granger Causality: A Review and Recent Advances
Introduced more than a half century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genomics and neuroscience.
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure
Originally developed for imputing missing entries in low rank, or approximately low rank matrices, matrix completion has proven widely effective in many problems where there is no reason to assume low-dimensional linear structure in the underlying matrix, as would be imposed by rank constraints.
Generalized Score Matching for General Domains
Score matching provides a powerful tool for estimating densities with such intractable normalizing constants, but as originally proposed is limited to densities on $\mathbb{R}^m$ and $\mathbb{R}_+^m$.
Statistical Inference for Networks of High-Dimensional Point Processes
The key ingredient for this inference procedure is a new concentration inequality on the first- and second-order statistics for integrated stochastic processes, which summarize the entire history of the process.
Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks
The optimal solution to this mathematical program is known to have desirable statistical properties under certain conditions.
Differential Network Analysis: A Statistical Perspective
Networks effectively capture interactions among components of complex systems, and have thus become a mainstay in many scientific disciplines.
Statistical significance in high-dimensional linear mixed models
Our framework is inspired by a recent line of work that proposes de-biasing penalized estimators to perform inference for high-dimensional linear models with fixed effects only.
Integer Programming for Learning Directed Acyclic Graphs from Continuous Data
In this paper, we study the problem of learning an optimal DAG from continuous observational data.
Generalized Sparse Additive Models
We present a unified framework for estimation and analysis of generalized additive models in high dimensions.
Wavelet regression and additive models for irregularly spaced data
We prove minimax optimal convergence rates under a weak compatibility condition for sparse additive models.
Generalized Score Matching for Non-Negative Data
The score matching method of Hyv\"arinen [2005] avoids direct calculation of the normalizing constant and yields closed-form estimates for exponential families of continuous distributions over $\mathbb{R}^m$.
The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks
In particular, it results in more efficient and accurate estimation in large networks containing hub nodes, which are common in biological systems.
Neural Granger Causality
We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data.
An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series
We present an efficient alternating direction method of multipliers (ADMM) algorithm for segmenting a multivariate non-stationary time series with structural breaks into stationary regions.
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery
A sufficient condition for Granger non-causality in this setting is that all of the outgoing weights of the input data, the past lags of a series, to the first hidden layer are zero.
In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference
In this paper, we consider a simple and very na\"{i}ve two-step procedure for this task, in which we (i) fit a lasso model in order to obtain a subset of the variables, and (ii) fit a least squares model on the lasso-selected set.
Nonparametric Regression with Adaptive Truncation via a Convex Hierarchical Penalty
We further establish minimax rates for a large class of sparse additive models.
Joint Estimation of Precision Matrices in Heterogeneous Populations
Further, to extend the applicability of the method to the settings with unknown populations structure, we propose a Laplacian penalty based on hierarchical clustering, and discuss conditions under which this data-driven choice results in consistent estimation of precision matrices in heterogenous populations.
Inference in High Dimensions with the Penalized Score Test
Further, when an $\ell_2$ penalty is used, the test corresponds precisely to a score test in a mixed effects model, in which the effects of all but one feature are assumed to be random.
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data.
The Cluster Graphical Lasso for improved estimation of Gaussian graphical models
We begin by introducing a surprising connection between the graphical lasso and hierarchical clustering: the graphical lasso in effect performs a two-step procedure, in which (1) single linkage hierarchical clustering is performed on the variables in order to identify connected components, and then (2) an l1-penalized log likelihood is maximized on the subset of variables within each connected component.
Penalized Principal Component Regression on Graphs for Analysis of Subnetworks
Network models are widely used to capture interactions among component of complex systems, such as social and biological.
