arXiv research

FlexCodeTS is a flexible time series density estimator.

Flexible empirical Bayes for large-scale multiple linear regression.

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

A new method combines experts' opinions to train regression models with noisy labels.

SPQR package uses neural networks for flexible quantile regression.

Neural Local Wasserstein Regression models distribution-on-distribution regression with flexible, localized transport maps.

Flexible framework for deep distributional regression models.

New method solves quantile crossing problem in econometrics.

flexBART improves BART for categorical predictors by creating flexible tree partitions.

Flexible Bayesian approach for generalized linear models, especially for sparse logistic regression.

Unified approach for interpretable regression with flexible modeling.

Flexible model for complex relationships using Bayesian nonparametrics.

Bayesian approach for estimating heterogeneous treatment effects in RDD designs.

VSPS creates flexible prediction regions for multi-target regression with guaranteed coverage.

The study compares different models for predicting factor premiums and finds neural networks perform better but have unstable weights.

To construct flexible nonlinear predictive distributions, the paper introduces a family of softplus function based regression models that convolve, stack, or combine both operations by convolving countably infinite stacked gamma distributions, whose scales depend on the covariates. Generalizing logistic regression that…

New method for efficient maximum likelihood estimation of $p$-generalized probit regression.

Study highlights how model choice affects uncertainty estimation in neural network regression.

Regression is widely used by practioners across many disciplines. We reformulate the underlying optimisation problem as a second-order conic program providing the flexibility often needed in applications. Using examples from portfolio management and quantitative trading we solve regression problems with and without con…

RegFlow models future states with flexible probability distributions.

Enforces physical constraints in GP regression models.

SRF improves kernel approximation and GP regression performance.

We introduce a new principle for model selection in regression and classification. Many regression models are controlled by some smoothness or flexibility or complexity parameter c, e.g. the number of neighbors to be averaged over in k nearest neighbor (kNN) regression or the polynomial degree in regression with polyno…

Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both. It is often beneficial to utilize flexible techniques such as semiparametric regression or machine learning to estimate these quantities. However, optimal estimation of these r…

Improves regression efficiency by separating material and immaterial parts of responses.

Finite mixtures of regression models offer a flexible framework for investigating heterogeneity in data with functional dependencies. These models can be conveniently used for unsupervised learning on data with clear regression relationships. We extend such models by imposing an eigen-decomposition on the multivariate …

Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible model specifications are needed as big(ger) data become available, facilitated by more powerful computing infrastructure. (2) Full probabilistic modeling rather than predicting just …

Enhances kernel regression with network data for better predictions.

Bayesian model improves classification performance with flexible uncertainty modeling.

Develops new algorithms for QRF to handle mixed-frequency and longitudinal data.

We propose a flexible nonparametric regression method for ultrahigh-dimensional data. As a first step, we propose a fast screening method based on the favored smoothing bandwidth of the marginal local constant regression. Then, an iterative procedure is developed to recover both the important covariates and the regress…

Estimates outcomes under hypothetical scenarios using a flexible framework.

Combines BART and Gaussian process for spatial covariate prediction with uncertainty.

Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized comp…

Although there is a rich literature on methods for allowing the variance in a univariate regression model to vary with predictors, time and other factors, relatively little has been done in the multivariate case. Our focus is on developing a class of nonparametric covariance regression models, which allow an unknown p …

This article proposes Multinomial Probit Bayesian Additive Regression Trees (MPBART) as a multinomial probit extension of BART - Bayesian Additive Regression Trees (Chipman et al (2010)). MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. T…

DRN improves actuarial distributional forecasting with interpretable neural networks.

SplitWise enhances stepwise regression by adaptively encoding numeric predictors into binary features.

Language models can predict numeric values as strings.

New algorithm for nonparametric IV regression using stochastic gradients.

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,…

We propose a simple yet powerful framework for modeling integer-valued data, such as counts, scores, and rounded data. The data-generating process is defined by Simultaneously Transforming and Rounding (STAR) a continuous-valued process, which produces a flexible family of integer-valued distributions capable of modeli…

It is shown that the the popular least squares method of option pricing converges even under very general assumptions. This substantially increases the freedom of creating different implementations of the method, with varying levels of computational complexity and flexible approach to regression. It is also argued that…

WDL models density curves using Wasserstein distance and flexible mixture models.

Riesz regression connects to density ratio estimation for causal inference.

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this paper, we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estima…

Deep Transformed Gaussian Processes extend TGPs with variational inference for scalable multi-layer modeling.

Simplifies IV regression for high-dimensional instruments.