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Found 26 papers, 10 papers with code
Predicting Rare Events by Shrinking Towards Proportional Odds
We present PRESTO, a relaxation of the proportional odds model for ordinal regression.
Generalized Data Thinning Using Sufficient Statistics
A recent paper showed that for some well-known natural exponential families, $X$ can be "thinned" into independent random variables $X^{(1)}, \ldots, X^{(K)}$, such that $X = \sum_{k=1}^K X^{(k)}$.
Inferring independent sets of Gaussian variables after thresholding correlations
Our proposed test properly accounts for the fact that the set of variables is selected from the data, and thus is not overly conservative.
Prediction Sets for High-Dimensional Mixture of Experts Models
Large datasets make it possible to build predictive models that can capture heterogenous relationships between the response variable and features.
Cluster Stability Selection
Stability selection (Meinshausen and Buhlmann, 2010) makes any feature selection method more stable by returning only those features that are consistently selected across many subsamples.
Controlling the False Split Rate in Tree-Based Aggregation
In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements.
Tree-based Node Aggregation in Sparse Graphical Models
High-dimensional graphical models are often estimated using regularization that is aimed at reducing the number of edges in a network.
Selective Inference for Hierarchical Clustering
Classical tests for a difference in means control the type I error rate when the groups are defined a priori.
Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions
The ocean is filled with microscopic microalgae called phytoplankton, which together are responsible for as much photosynthesis as all plants on land combined.
Testing for Association in Multi-View Network Data
To answer this question, we extend the stochastic block model for a single network view to the two-view setting, and develop a new hypothesis test for the null hypothesis that the latent community memberships in the two data views are independent.
Are Clusterings of Multiple Data Views Independent?
However, clustering the participants based on multiple data views implicitly assumes that a single underlying clustering of the participants is shared across all data views.
Rare Feature Selection in High Dimensions
It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events.
Estimating the error variance in a high-dimensional linear model
In this paper, we propose the natural lasso estimator for the error variance, which maximizes a penalized likelihood objective.
Valid Inference Corrected for Outlier Removal
Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers.
Interpretable Vector AutoRegressions with Exogenous Time Series
The Vector AutoRegressive (VAR) model is fundamental to the study of multivariate time series.
BigVAR: Tools for Modeling Sparse High-Dimensional Multivariate Time Series
The R package BigVAR allows for the simultaneous estimation of high-dimensional time series by applying structured penalties to the conventional vector autoregression (VAR) and vector autoregression with exogenous variables (VARX) frameworks.
Computation
The Simulator: An Engine to Streamline Simulations
The syntax of the simulator leads to simulation code that is easily human-readable.
Graph-Guided Banding of the Covariance Matrix
A highly-related, yet complementary, literature studies the specific setting in which the measured variables have a known ordering, in which case a banded population matrix is often assumed.
Learning Local Dependence In Ordered Data
Penalized maximum likelihood estimation of this matrix yields a simple regression interpretation for local dependence in which variables are predicted by their neighbors.
Non-convex Global Minimization and False Discovery Rate Control for the TREX
The TREX is a recently introduced method for performing sparse high-dimensional regression.
Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations
The purpose of this paper is to provide a side-by-side comparison of these two frameworks for HSM in terms of their statistical properties and computational efficiency.
High Dimensional Forecasting via Interpretable Vector Autoregression
Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series.
Sparse Partially Linear Additive Models
Thus, to make a GPLAM a viable approach in situations in which little is known $a~priori$ about the features, one must overcome two primary model selection challenges: deciding which features to include in the model and determining which of these features to treat nonlinearly.
Convex Banding of the Covariance Matrix
Empirical studies demonstrate its practical effectiveness and illustrate that our exactly-banded estimator works well even when the true covariance matrix is only close to a banded matrix, confirming our theoretical results.
A lasso for hierarchical interactions
We add a set of convex constraints to the lasso to produce sparse interaction models that honor the hierarchy restriction that an interaction only be included in a model if one or both variables are marginally important.
CUR from a Sparse Optimization Viewpoint
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns of X.
