arXiv research

This paper reviews SDR methods for multivariate response regression.

Study GLS estimator properties in multivariate regression with heteroskedastic and autocorrelated errors.

Extends online linear regression to handle multivariate data.

Multivariate regression model is a natural generalization of the classical univari- ate regression model for fitting multiple responses. In this paper, we propose a high- dimensional multivariate conditional regression model for constructing sparse estimates of the multivariate regression coefficient matrix that accoun…

The paper analyzes the generalizability of linear autoencoders and multivariate linear regression.

The paper introduces new estimators for multivariate functions using Fourier methods.

Bayesian neural network models improve uncertainty quantification in multivariate regression.

Proposes a multivariate regression model for better analysis of multiple datasets.

This study compares multivariate vs univariate machine learning for multi-output regression.

Develops a new multivariate regression model for complex outcomes.

NGBoost boosts multivariate probabilistic regression.

New method for multivariate distribution regression using NPT metric.

We consider the topic of multivariate regression on manifold-valued output, that is, for a multivariate observation, its output response lies on a manifold. Moreover, we propose a new regression model to deal with the presence of grossly corrupted manifold-valued responses, a bottleneck issue commonly encountered in pr…

TSRGA scales multivariate linear regression for feature-distributed data.

Improved robustness in multivariate regression and classification with DRO under Wasserstein metric.

SMART combines decision trees and MARS for better regression modeling.

Neural optimal transport improves multivariate conformal prediction.

Study uses regression and ML for COVID-19 mortality forecasting.

Combines MCTM and NF for flexible multivariate density regression with interpretable marginals.

Bayesian approach for multivariate density regression of complex data.

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tu…

We propose a family of multivariate Gaussian process models for correlated outputs, based on assuming that the likelihood function takes the generic form of the multivariate exponential family distribution (EFD). We denote this model as a multivariate generalized Gaussian process model, and derive Taylor and Laplace al…

Improved local multivariable regression for better inference with limited data.

FineMorphs models smooth transformations for multivariate regression.

Regularizes ML algorithms for robust multivariate analysis against distribution shifts.

Develops methods for selecting and estimating smooth functional coefficients in high-dimensional multivariate functional data.

In multivariate regression, a $K$-dimensional response vector is regressed upon a common set of $p$ covariates, with a matrix $B^*\in\mathbb{R}^{p\times K}$ of regression coefficients. We study the behavior of the multivariate group Lasso, in which block regularization based on the $\ell_1/\ell_2$ norm is used for supp…

Bayesian DDR models complex multivariate distributions.

Robust multivariate linear regression methods for online and offline use.

Software helps teach latent variable methods in multivariate data analytics.

New method improves calibration in multi-output probabilistic models.

Proposes MinPEN framework for estimating relationships in multivariate models.

The paper proposes a new auto-regressive model for multivariate distributional time series.

Improves MARS for nonparametric multivariate regression with dimension reduction.

EP method speeds up Bayesian probit regression in high dimensions.

Optimizes minimum-volume prediction sets for multivariate regression.

The Morse-Smale complex of a function $f$ decomposes the sample space into cells where $f$ is increasing or decreasing. When applied to nonparametric density estimation and regression, it provides a way to represent, visualize, and compare multivariate functions. In this paper, we present some statistical results on es…

Proposes a new model for non-linear regression of multivariate time series data.

TQF models multivariate uncertainty by learning conditional quantiles.

New online method for multivariate probabilistic electricity price forecasting.

Proposes a new method for multivariate functional regression.

New matching estimators correct bias in multivariate settings without smoothing parameters.

This paper introduces a novel recalibration method for multivariate forecasts.

In this paper, we consider a generalized multivariate regression problem where the responses are monotonic functions of linear transformations of predictors. We propose a semi-parametric algorithm based on the ordering of the responses which is invariant to the functional form of the transformation function. We prove t…

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables,…

Bayesian method for knot inference in multivariate spline regression.

Extends multivariate regression for tensor-variate data, identifying brain regions and facial characteristics.

Optimal transport improves multivariate prediction uncertainty quantification.