Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.08157

TEASE: Targeted elastic spatial envelope

Mitchell L. Krock
View original source ↗Read source document ↗Export metadata ↓

Abstract

Envelope regression is a crucial part of multivariate linear modeling, leveraging separation into material and immaterial parts to provide parsimonious data reduction. Rekabdarkolaee et al. (2020) construct spatial envelope, a novel multivariate Gaussian process, extended by May et al. (2022) to linear coregionalization envelope. Our proposal (TEASE) combines spatial envelope and full-scale basis graphical lasso (LeDuc et al., 2025), leveraging targeted graphical elastic net (Kovács et al., 2021) for immaterial precision matrices. Material part is a multivariate multiresolution Gaussian Markov random field (Kleiber et al., 2019; Caringi and Secchi, 2026). Enhanced envelope penalty (Kwon and Zou, 2025) regularizes regression coefficients. Demonstrations are on climate data.

Explore related subjects

stat.ME

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mitchell L. Krock. 2026-10-06. TEASE: Targeted elastic spatial envelope. https://arxiv.org/abs/2610.08157

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Reframing cross-world independence in multimediator causal models

Multi-mediator causal models provide a general framework for decomposing causal effects along specific mechanisms, but effect identification in the presence of treatment-induced confounding remains conceptually challenging. Path-specific effects (PSEs) provide one important formulation of such causal decomposition. In particular, existing approaches differ substantially in their counterfactual assumptions and in the interventions used to define effects through specific pathways. We use Single-World Intervention Graphs (SWIGs) as a unifying analytic device to formalize and compare three approaches to PSE identification: classical path-specific effects based on cross-world independence assumptions, interventional effects based on randomized mediator interventions, and separable effects based on decomposing the exposure into distinct causal components. Through this unified analysis, we show that these seemingly different approaches can, under appropriate conditions, lead to the same identification formulas despite relying on different causal assumptions and intervention semantics. The SWIG representation further reveals how each approach resolves the counterfactual conflicts created by treatment-induced confounding. Our results highlight a fundamental distinction between statistical identification and causal interpretation: the same observed-data functional can represent different causal questions depending on how the underlying counterfactual worlds are constructed. This unified perspective clarifies relationships among major approaches in multimediator settings and guides alignment of the estimand, identification assumptions, and scientific mechanism of interest.

stat.ME↗
arXiv2026

Function on Scalar Regression with Complex Survey Designs

Large health surveys increasingly collect high-dimensional functional data from wearable devices, and function on scalar regression (FoSR) is used to quantify the relationship between these functional outcomes and scalar covariates like age and sex. However, existing methods for FoSR fail to account for complex survey design. We introduce inferential methods for FoSR with complex survey designs. The approach combines fast univariate inference (FUI) developed for functional outcomes and survey sampling inferential methods developed for scalar outcomes. Our approach consists of three steps: (1) fit survey weighted GLMs at each point along the functional domain, (2) smooth coefficients along the functional domain, and (3) use balanced repeated replication (BRR) or Rao-Wu-Yue-Beaumont (RWYB) bootstrap to obtain pointwise and joint confidence bands for the functional coefficients. The approach is motivated by association studies between continuous physical activity data and covariates collected in the National Health and Nutrition Examination Survey (NHANES). A first-of-its-kind analytical simulation study and empirical simulation using NHANES data demonstrates that our approach performs better than existing methods that do not account for the survey structure. Finally, application of the approach in NHANES shows the practical implications of accounting for survey structure. The approach is implemented in the R package \texttt{svyfosr}.

stat.ME↗
arXiv2026

Bayesian Additive Regression Tree Copula Processes for Scalable Distributional Prediction

We show how to construct the implied copula process of response values from a Bayesian additive regression tree (BART) model with prior on the leaf node variances. This copula process, defined on the covariate space, can be combined with any marginal distribution for the dependent variable to construct a flexible distributional BART model. Bayesian inference is performed via Markov chain Monte Carlo on an augmented posterior, where we show that key sampling steps can be realized as those of Chipman et al. (2010), preserving scalability and computational efficiency despite the high dimensionality of the copula process. The posterior predictive distribution from the copula process model is derived in closed form as the push-forward of the posterior predictive distribution of the underlying BART model with an optimal transport map. Under suitable conditions, and allowing for model misspecification, we show that the model-implied conditional density closest in Kullback-Leibler divergence to the true data-generating process exists, is unique and has a closed form expression. We further establish that fractional posteriors for the conditional densities at the observed covariate values concentrate around this projection at a rate that adapts to the smoothness of the corresponding regression function and to the number of relevant covariates. Simulation studies demonstrate improved accuracy of distributional predictions compared to the original BART model and leading benchmarks. Applications to five real datasets with 506 to 515,345 observations and 8 to 90 covariates further highlight the efficacy and scalability of our proposed BART copula process model.

stat.ME↗