11 citations · 16 across the 9 of their papers we have counts for
10 papers
Deep-testing: the case of dependence detection
Gery Geenens, Pierre Lafaye de Micheaux, Ivan Muyun Zou
Deep learning methods have proved highly effective for classification and image recognition problems. In this paper, we ask whether this success can be transferred to hypothesis te…
tBayes-MICE: A Bayesian Approach to Multiple Imputation for Time Series Data
Amuche Ibenegbu, Pierre Lafaye de Micheaux, Rohitash Chandra
Time-series analysis is often affected by missing data, a common problem across several fields, including healthcare and environmental monitoring. Multiple Imputation by Chained Eq…
Adaptive Heavy-Tailed Stochastic Gradient Descent
Bodu Gong, Gustavo Enrique Batista, Pierre Lafaye de Micheaux
In the era of large-scale neural network models, optimization algorithms often struggle with generalization due to an overreliance on training loss. One key insight widely accepted…
Generative Flexible Latent Structure Regression (GFLSR) model
Clara Grazian, Qian Jin, Pierre Lafaye De Micheaux
Latent structure methods, specifically linear continuous latent structure methods, are a type of fundamental statistical learning strategy. They are widely used for dimension reduc…
A linear regression model for quantile function data applied to paired pulmonary 3d CT scans
Marie-Félicia Béclin, Pierre Lafaye de Micheaux, Nicolas Molinari +1
This paper introduces a new objective measure for assessing treatment response in asthmatic patients using computed tomography (CT) imaging data. For each patient, CT scans were ob…
Counterexamples to the classical central limit theorem for triplewise independent random variables having a common arbitrary margin
Guillaume Boglioni Beaulieu, Pierre Lafaye de Micheaux, Frédéric Ouimet
We present a general methodology to construct triplewise independent sequences of random variables having a common but arbitrary marginal distribution (satisfying very mild con…