arXiv research

The paper tackles high-dimensional mixed linear regression with unknown parameters and proposes methods for estimation, confidence intervals, and hypothesis testing.

Paper introduces a new IV regression method for mixed-frequency data.

New AMP algorithm estimates signals and latent variables in mixed regression models.

A new method combines machine learning with mixed-effects models for better repeated measurement analysis.

New insights into statistical and computational limits for mixed sparse linear regression.

We study the computational complexity of Markov chain Monte Carlo (MCMC) methods for high-dimensional Bayesian linear regression under sparsity constraints. We first show that a Bayesian approach can achieve variable-selection consistency under relatively mild conditions on the design matrix. We then demonstrate that t…

Mix-IRLS solves imbalanced mixed linear regression problems efficiently.

Linear mixed models (LMMs) are used extensively to model dependecies of observations in linear regression and are used extensively in many application areas. Parameter estimation for LMMs can be computationally prohibitive on big data. State-of-the-art learning algorithms require computational complexity which depends …

Study improves ERM for heavy-tailed data with dependent inputs.

Paper analyzes agnostic learning of mixed linear regression without generative models.

Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various confounding factors such as age, ethnicity and population structure…

The study examines mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression.

A DP method selects best sparse models in high dimensions efficiently.

AM converges super-linearly for solving mixed linear regression problems.

Wasserstein framework solves mixed linear regression problems.

We focus on the high-dimensional linear regression problem, where the algorithmic goal is to efficiently infer an unknown feature vector $β^*\in\mathbb{R}^p$ from its linear measurements, using a small number $n$ of samples. Unlike most of the literature, we make no sparsity assumption on $β^*$, but instead adopt a dif…

Method improves regression models using unlabeled data.

Study bounds noise level in linear regression with dependent data.

Gradient EM converges exponentially to optimal solution in agnostic mixtures.

The Expectation-Maximization algorithm is perhaps the most broadly used algorithm for inference of latent variable problems. A theoretical understanding of its performance, however, largely remains lacking. Recent results established that EM enjoys global convergence for Gaussian Mixture Models. For Mixed Linear Regres…

The paper forecasts corporate distress using a novel MIDAS logistic regression method.

Unified derivation of high-dimensional linear models using stochastic gradient descent.

We simplify complex regression coefficients using linearization and feature comparison.

Gradient Boosted Mixed Models estimate mean and variance components for clustered data.

Paper analyzes EM algorithm's trajectory in 2MLR, revealing cycloid behavior.

Paper introduces semi-supervised linear extremile regression for high-dimensional data.

Efficient Bayesian LMM framework for high-dimensional longitudinal data.

Paper solves NP-hard sparse mixed linear regression problem with provable guarantees.

This paper uses MIO to select features for kernel SVM classification.

Paper develops a novel approach for classifying high-dimensional mixed data.

Nested model averaging improves high-dimensional linear regression performance.

A fast MCMC sampler for sparse Bayesian inference.

A scalable model for high-dimensional longitudinal data.

Paper addresses online identification and clustering for mixed linear regression models.

Given a linear regression setting, Iterative Least Trimmed Squares (ILTS) involves alternating between (a) selecting the subset of samples with lowest current loss, and (b) re-fitting the linear model only on that subset. Both steps are very fast and simple. In this paper we analyze ILTS in the setting of mixed linear …

We propose a novel high-dimensional linear regression estimator: the Discrete Dantzig Selector, which minimizes the number of nonzero regression coefficients subject to a budget on the maximal absolute correlation between the features and residuals. Motivated by the significant advances in integer optimization over the…

Study shows double descent curve in high-dimensional linear regression with random projections.

Bayesian econometrics improves nowcasting during pandemics.

Study high-dimensional Bayesian linear regression using variational inference.

Latent variable models are a fundamental modeling tool in machine learning applications, but they present significant computational and analytical challenges. The popular EM algorithm and its variants, is a much used algorithmic tool; yet our rigorous understanding of its performance is highly incomplete. Recently, wor…

Paper develops a new estimator for high-dimensional panel data with common shocks.

Lecture notes on advanced linear regression methods.

This paper concerns the development of an inferential framework for high-dimensional linear mixed effect models. These are suitable models, for instance, when we have $n$ repeated measurements for $M$ subjects. We consider a scenario where the number of fixed effects $p$ is large (and may be larger than $M$), but the n…

PROBE algorithm efficiently solves sparse high-dimensional linear regression.

The study examines robustness auditing for linear regression, improving existing methods and identifying computational challenges.

The nullspace and regularization impact high-dimensional linear regression interpretability.

New method bounds high-dimensional regression without estimating design covariance.

New method for high-dimensional linear regression using empirical Bayes.