65 citations · 229 across the 33 of their papers we have counts for
36 papers
Principal Components Decomposition of Fraction of Variance Explained in High Dimensional Linear Models with Strong Correlation
Man Luo, Chun Chieh Fan, David Azriel +1
The fraction of variance explained (FVE) in a linear model quantifies the extent to which predictors account for outcome variability. In high-dimensional settings, where traditiona…
SCoRES: An R Package for Simultaneous Confidence Region Estimates
Zhuoran Yu, Armin Schwartzman, Junting Ren +1
The identification of domain sets whose outcomes belong to predefined subsets can address fundamental risk assessment challenges in climatology and medicine. Existing approaches fo…
Model uncertainty quantification using feature confidence sets for outcome excursions
Junting Ren, Armin Schwartzman
When implementing prediction models for high-stakes real-world applications such as medicine, finance, and autonomous systems, quantifying prediction uncertainty is critical for ef…
Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB
Anas Dorbani, Sunny Yasser, Jimmy Lin +1
Knowledge-intensive analytical applications retrieve context from both structured tabular data and unstructured, text-free documents for effective decision-making. Large language m…
Consistency of heritability estimation from summary statistics in high-dimensional linear models
David Azriel, Samuel Davenport, Armin Schwartzman
In Genome-Wide Association Studies (GWAS), heritability is defined as the fraction of variance of an outcome explained by a large number of genetic predictors in a high-dimensional…
On the peak height distribution of non-stationary Gaussian random fields: 1D general covariance and scale space
Yu Zhao, Dan Cheng, Samuel Davenport +1
We study the peak height distribution of certain non-stationary Gaussian random fields. The explicit peak height distribution of smooth, non-stationary Gaussian processes in 1D wit…