arXiv research

Develops inequalities for high-dimensional linear processes with dependent innovations.

Study improves financial risk assessment using ARMA-APARCH-EVT models with HACs.

Fair HAC algorithms ensure clustering fairness across protected groups.

Data aggregation improves HAC for resource-constrained systems.

We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the ti…

Evaluating AI investment strategies

Proposes a method to make statistical inferences robust in spatially dependent settings with missing at random labels.

Hierarchical clustering is a widely used approach for clustering datasets at multiple levels of granularity. Despite its popularity, existing algorithms such as hierarchical agglomerative clustering (HAC) are limited to the offline setting, and thus require the entire dataset to be available. This prohibits their use o…

As the size $n$ of datasets become massive, many commonly-used clustering algorithms (for example, $k$-means or hierarchical agglomerative clustering (HAC) require prohibitive computational cost and memory. In this paper, we propose a solution to these clustering problems by extending threshold clustering (TC) to probl…

New method needed for class prior estimation when covariates are reduced.

DACE estimates covariance from compressed data, improving accuracy.

Paper proposes a new covariance estimator ensuring positive semi-definite matrices.

Method estimates sparse inverse covariance and partial correlation matrices efficiently.

We propose a novel estimation approach for the covariance matrix based on the $l_1$-regularized approximate factor model. Our sparse approximate factor (SAF) covariance estimator allows for the existence of weak factors and hence relaxes the pervasiveness assumption generally adopted for the standard approximate factor…

In many applications that involve processing high-dimensional data, it is important to identify a small set of entities that account for a significant fraction of detections. Rather than formalize this as a clustering problem, in which all detections must be grouped into hard or soft categories, we formalize it as an i…

New methods estimate covariance for matrix data without assuming fixed size or specific distributions.

Extends covariance estimation with multiple targets for better performance.

This paper presents a new method for estimating high dimensional covariance matrices. The method, permuted rank-penalized least-squares (PRLS), is based on a Kronecker product series expansion of the true covariance matrix. Assuming an i.i.d. Gaussian random sample, we establish high dimensional rates of convergence to…

CovRegRF estimates covariance matrix from covariates using random forests.

New method improves covariance estimation for weighted samples.

Efficiently estimates covariance for sparse functional data.

Paper presents a new framework for covariance matrix estimation with geometric insights.

Proposes a robust method for predicting missing outcomes in covariate shift adaptation.

Study nonparametric covariance function estimation for noisy data.

Paper develops an online covariance estimator for nonsmooth stochastic approximation problems.

The graphical lasso (glasso) is a widely-used fast algorithm for estimating sparse inverse covariance matrices. The glasso solves an L1 penalized maximum likelihood problem and is available as an R library on CRAN. The output from the glasso, a regularized covariance matrix estimate a sparse inverse covariance matrix e…

New estimator handles covariate shift with closed-form solution and super-efficiency.

New covariance estimator for financial portfolios.

New method improves online covariance estimation for SGD.

Paper tackles CATE estimation with missing treatment info.

PACE-GGM uses Gaussian mechanism for private covariance estimation.

Study on estimating distances between covariance operators and Gaussian processes.

New method estimates covariance matrices without restrictive assumptions.

Two new covariance estimators for ROOT-SGD improve statistical inference.

The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empirically compare the performance of 9 improved covariance estimation procedures by using daily returns of 90 highly capitalized US stocks for th…

SCOPE estimator improves covariance and precision matrix estimation.

We consider the problem of joint estimation of structured inverse covariance matrices. We perform the estimation using groups of measurements with different covariances of the same unknown structure. Assuming the inverse covariances to span a low dimensional linear subspace in the space of symmetric matrices, our aim i…

The application of standard sufficient dimension reduction methods for reducing the dimension space of predictors without losing regression information requires inverting the covariance matrix of the predictors. This has posed a number of challenges especially when analyzing high-dimensional data sets in which the numb…

New methods improve portfolio risk minimization by estimating covariance matrix more accurately.

Paper optimizes private PCA for covariance estimation in statistics.

New algorithm improves matrix estimation with one-sided covariates.

This paper focuses on the estimation of the sample covariance matrix from low-dimensional random projections of data known as compressive measurements. In particular, we present an unbiased estimator to extract the covariance structure from compressive measurements obtained by a general class of random projection matri…

Improved 2-bit covariance estimator with reduced operator norm error and no tuning needed.

New method for estimating covariance with robustness to outliers.

Robustly estimates linear regression coefficients with adversarial and noisy data.

New method balances covariates for stable causal survival effect estimation.

Lower bounds on private estimation of Gaussian covariance matrices.

Paper tackles unbounded density ratio estimation for covariate shift adaptation.