Nicholas Gaubatz | Searcharxiv

arXiv2026

Subarrangements of type A: the weak Lefschetz property of the Artinian Orlik-Terao algebra

In 1994, Orlik and Terao introduced a commutative Artinian analog S/I(A) of the Orlik-Solomon algebra of a hyperplane arrangement A to answer a question of Aomoto. A central topic of investigation in the study of Artinian algebras is the Weak Lefschetz Property (WLP). We analyze WLP for the Artinian Orlik-Terao algebra of graphc arrangements. Even for chordal graphs (which give rise to Koszul algebras) WLP sometimes fails; conversely an analysis of the state polytope shows WLP can hold even when WLP fails for all possible initial ideals. More generally, for any algebra with a tensor product decomposition, we construct canonical elements in the kernel of the multiplication map, refining previous results in the literature.

math.AC↗

arXiv2026

Unified Operator Framework for Functional and Multivariate Regression

We develop a unified operator framework for scalar, multivariate, and functional regression based on integral operators defined with respect to general measures. Within this framework, classical regression models, including scalar-on-function, function-on-scalar, function-on-function, and multivariate multiple regression, arise as special cases corresponding to different choices of input and output measures. We establish three main results. First, we show that the standard regression taxonomy can be expressed as a single operator under varying measures. Second, we demonstrate that discrete representations correspond to exact operator evaluations under discrete measures and converge to the continuous operator as the observation grid is refined. Third, we show that estimation under the discrete-measure formulation reduces to standard multivariate regression, with statistical properties governed by classical results. A simulation study illustrates these principles, highlighting the roles of discretization, conditioning, and estimation. Overall, the proposed framework clarifies the relationship between functional and multivariate regression and provides a meaningful interpretation of discretized modeling approaches as operator estimation under different measure specifications. This perspective also explains why vectorized multivariate regression is often competitive with functional methods in linear settings: it directly estimates the discrete-measure representation of the underlying operator.

stat.ME↗