b'Art B. Owen'

research

∙ 09/08/2023

Computable error bounds for quasi-Monte Carlo using points with non-negative local discrepancy

Let f:[0,1]^d→ℝ be a completely monotone integrand as defined by Aistlei...

0 Michael Gnewuch, et al. ∙

research

∙ 08/30/2023

Scalable Estimation of Probit Models with Crossed Random Effects

Crossed random effects structures arise in many scientific contexts. The...

0 Ruggero Bellio, et al. ∙

research

∙ 08/15/2023

Gain coefficients for scrambled Halton points

Randomized quasi-Monte Carlo, via certain scramblings of digital nets, p...

0 Art B. Owen, et al. ∙

research

∙ 07/23/2023

Scalable solution to crossed random effects model with random slopes

The crossed random-effects model is widely used in applied statistics, f...

0 Disha Ghandwani, et al. ∙

research

∙ 02/19/2023

Mean dimension of radial basis functions

We show that generalized multiquadric radial basis functions (RBFs) on ℝ...

0 Christopher Hoyt, et al. ∙

research

∙ 11/15/2022

Model free Shapley values for high dimensional data

A model-agnostic variable importance method can be used with arbitrary p...

0 Naofumi Hama, et al. ∙

research

∙ 08/09/2022

Super-polynomial accuracy of multidimensional randomized nets using the median-of-means

We study approximate integration of a function f over [0,1]^s based on t...

0 Zexin Pan, et al. ∙

research

∙ 05/31/2022

Variable importance without impossible data

The most popular methods for measuring importance of the variables in a ...

0 Masayoshi Mase, et al. ∙

research

∙ 05/25/2022

Deletion and Insertion Tests in Regression Models

A basic task in explainable AI (XAI) is to identify the most important f...

0 Naofumi Hama, et al. ∙

research

∙ 02/25/2022

A general characterization of optimal tie-breaker designs

In a regression discontinuity design, subjects with a running variable x...

0 Harrison H. Li, et al. ∙

research

∙ 02/21/2022

Optimality in Multivariate Tie-breaker Designs

Tie-breaker designs (TBDs), in which subjects with extreme values are as...

0 Tim P. Morrison, et al. ∙

research

∙ 02/06/2022

Pre-integration via Active Subspaces

Pre-integration is an extension of conditional Monte Carlo to quasi-Mont...

0 Sifan Liu, et al. ∙

research

∙ 12/27/2021

Combining randomized field experiments with observational satellite data to assess the benefits of crop rotations on yields

With climate change threatening agricultural productivity and global foo...

0 Dan M. Kluger, et al. ∙

research

∙ 11/24/2021

Super-polynomial accuracy of one dimensional randomized nets using the median-of-means

Let f be analytic on [0,1] with |f^(k)(1/2)|≤ Aα^kk! for some constant A...

0 Zexin Pan, et al. ∙

research

∙ 10/13/2021

Where are the logs?

The commonly quoted error rates for QMC integration with an infinite low...

0 Art B. Owen, et al. ∙

research

∙ 07/27/2021

Probing neural networks with t-SNE, class-specific projections and a guided tour

We use graphical methods to probe neural nets that classify images. Plot...

16 Christopher R. Hoyt, et al. ∙

research

∙ 06/19/2021

The nonzero gain coefficients of Sobol's sequences are always powers of two

When a plain Monte Carlo estimate on n samples has variance σ^2/n, then ...

0 Zexin Pan, et al. ∙

research

∙ 05/28/2021

Scalable logistic regression with crossed random effects

The cost of both generalized least squares (GLS) and Gibbs sampling in a...

0 Swarnadip Ghosh, et al. ∙

research

∙ 05/17/2021

What makes you unique?

This paper proposes a uniqueness Shapley measure to compare the extent t...

0 Benjamin B. Seiler, et al. ∙

research

∙ 05/15/2021

Cohort Shapley value for algorithmic fairness

Cohort Shapley value is a model-free method of variable importance groun...

0 Masayoshi Mase, et al. ∙

research

∙ 04/18/2021

A central limit theorem for the Benjamini-Hochberg false discovery proportion under a factor model

The Benjamini-Hochberg (BH) procedure remains widely popular despite hav...

0 Dan M. Kluger, et al. ∙

research

∙ 04/07/2021

Quasi-Newton Quasi-Monte Carlo for variational Bayes

Many machine learning problems optimize an objective that must be measur...

0 Sifan Liu, et al. ∙

research

∙ 02/20/2021

Designing Experiments Informed by Observational Studies

The increasing availability of passively observed data has yielded a gro...

0 Evan Rosenman, et al. ∙

research

∙ 01/23/2021

Local linear tie-breaker designs

Tie-breaker experimental designs are hybrids of Randomized Control Trial...

0 Dan M. Kluger, et al. ∙

research

∙ 08/18/2020

On dropping the first Sobol' point

Quasi-Monte Carlo (QMC) points are a substitute for plain Monte Carlo (M...

0 Art B. Owen, et al. ∙

research

∙ 07/21/2020

Backfitting for large scale crossed random effects regressions

Regression models with crossed random effect error models can be very ex...

0 Swarnadip Ghosh, et al. ∙

research

∙ 07/02/2020

Efficient estimation of the ANOVA mean dimension, with an application to neural net classification

The mean dimension of a black box function of d variables is a convenien...

0 Christopher Hoyt, et al. ∙

research

∙ 02/18/2020

A strong law of large numbers for scrambled net integration

This article provides a strong law of large numbers for integration on d...

0 Art B. Owen, et al. ∙

research

∙ 11/01/2019

Explaining black box decisions by Shapley cohort refinement

We introduce a variable importance measure to explain the importance of ...

0 Masayoshi Mase, et al. ∙

research

∙ 07/01/2019

Mean Dimension of Ridge Functions

We consider the mean dimension of some ridge functions of spherical Gaus...

0 Christopher R. Hoyt, et al. ∙

research

∙ 01/18/2019

Unreasonable effectiveness of Monte Carlo

This is a comment on the article "Probabilistic Integration: A Role in S...

0 Art B. Owen, et al. ∙

research

∙ 01/10/2019

The square root rule for adaptive importance sampling

In adaptive importance sampling, and other contexts, we have unbiased an...

0 Art B. Owen, et al. ∙

research

∙ 08/22/2018

Optimizing the tie-breaker regression discontinuity design

Motivated by customer loyalty plans, we study tie-breaker designs which ...

0 Art B. Owen, et al. ∙

research

∙ 07/16/2018

Density estimation by Randomized Quasi-Monte Carlo

We consider the problem of estimating the density of a random variable X...

0 Amal Ben Abdellah, et al. ∙

research

∙ 04/20/2018

Propensity Score Methods for Merging Observational and Experimental Datasets

We consider merging information from a randomized controlled trial (RCT)...

0 Evan Rosenman, et al. ∙

research

∙ 11/11/2017

Deterministic parallel analysis

Factor analysis is widely used in many application areas. The first step...

0 Edgar Dobriban, et al. ∙

research

∙ 10/19/2017

Importance sampling the union of rare events with an application to power systems analysis

This paper presents a method for estimating the probability μ of a union...

0 Art B. Owen, et al. ∙

research

∙ 06/09/2017

A randomized Halton algorithm in R

Randomized quasi-Monte Carlo (RQMC) sampling can bring orders of magnitu...

0 Art B. Owen, et al. ∙

research

∙ 10/27/2015

Statistically efficient thinning of a Markov chain sampler

It is common to subsample Markov chain output to reduce the storage burd...

0 Art B. Owen, et al. ∙

research

∙ 06/08/2011

Moment based estimation of stochastic Kronecker graph parameters

Stochastic Kronecker graphs supply a parsimonious model for large sparse...

0 David F. Gleich, et al. ∙

Art B. Owen

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