arXiv subjects

Aurélie Labbe

Publications and source records attributed to Aurélie Labbe.

5 recordsLinked to original sources

arXiv2026

Incomplete Matrix Regression

Matrix completion seeks to recover a low-rank matrix from a sparse and noisy subset of its entries. In many applications, such as recommendation systems and urban mobility, the observed matrix is accompanied by auxiliary covariates on its rows and columns and exhibits dependence across them. We propose Incomplete Matrix Regression (IMR), a distribution-free penalized regression framework that integrates such information into matrix completion. The target matrix is modeled as the sum of intercepts, covariate effects regularized by a Lasso penalty, and a low-rank latent component that captures structure unexplained by the covariates. Known similarity structures, such as spatial and temporal kernels, are incorporated through ridge-type penalties on the latent factors. For estimation, we provide a scalable alternating least-squares algorithm whose modular form allows us to include or exclude individual model components without rederiving the updates. We establish non-asymptotic error bounds for both the Lasso and matrix completion estimators that are consistent with standard rates in their respective literature. Through simulation studies and two real-data applications, we demonstrate that the proposed method attains predictive accuracy competitive with more complex methods at a small fraction of their computational cost. The methodology is implemented in the R package IMR.

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arXiv2026

A Bayesian Framework for Post-disruption Travel Time Prediction in Metro Networks

Disruptions are an inherent feature of transportation systems, occurring unpredictably and with varying durations. Even after an incident is reported as resolved, disruptions can induce irregular train operations that generate substantial uncertainty in passenger waiting and travel times. Accurately forecasting post-disruption travel times therefore remains a critical challenge for transit operators and passenger information systems. This paper develops a Bayesian spatiotemporal modeling framework for post-disruption train travel times that explicitly captures train interactions, headway imbalance, and non-Gaussian distributional characteristics observed during recovery periods. The proposed model decomposes travel times into delay and journey components and incorporates a moving-average error structure to represent dependence between consecutive trains. Skew-normal and skew-$t$ distributions are employed to flexibly accommodate heteroskedasticity, skewness, and heavy-tailed behavior in post-disruption travel times. The framework is evaluated using high-resolution track-occupancy and disruption log data from the Montréal metro system, covering two lines in both travel directions. Empirical results indicate that post-disruption travel times exhibit pronounced distributional asymmetries that vary with traveled distance, as well as significant error dependence across trains. The proposed models consistently outperform baseline specifications in both point prediction accuracy and uncertainty quantification, with the skew-$t$ model demonstrating the most robust performance for longer journeys. These findings underscore the importance of incorporating both distributional flexibility and error dependence when forecasting post-disruption travel times in urban rail systems.

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arXiv2021

Detecting differentially methylated regions in bisulfite sequencing data using quasi-binomial mixed models with smooth covariate effect estimates

Identifying disease-associated changes in DNA methylation can help to gain a better understanding of disease etiology. Bisulfite sequencing technology allows the generation of methylation profiles at single base of DNA. We previously developed a method for estimating smooth covariate effects and identifying differentially methylated regions (DMRs) from bisulfite sequencing data, which copes with experimental errors and variable read depths; this method utilizes the binomial distribution to characterize the variability in the methylated counts. However, bisulfite sequencing data frequently include low-count integers and can exhibit over or under dispersion relative to the binomial distribution. We present a substantial improvement to our previous work by proposing a quasi-likelihood-based regional testing approach which accounts for multiplicative and additive sources of dispersion. We demonstrate the theoretical properties of the resulting tests, as well as their marginal and conditional interpretations. Simulations show that the proposed method provides correct inference for smooth covariate effects and captures the major methylation patterns with excellent power.

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arXiv2017

Transmission clusters in the HIV-1 epidemic among men who have sex with men in Montreal, Quebec, Canada

Background. Several studies have used phylogenetics to investigate Human Immunodeficiency Virus (HIV) transmission among Men who have Sex with Men (MSMs) in Montreal, Quebec, Canada, revealing many transmission clusters. The Quebec HIV genotyping program sequence database now includes viral sequences from close to 4,000 HIV-positive individuals classified as MSMs. In this paper, we investigate clustering in those data by comparing results from several methods: the conventional Bayesian and maximum likelihood-bootstrap methods, and two more recent algorithms, DM-PhyClus, a Bayesian algorithm that produces a measure of uncertainty for proposed partitions, and the Gap Procedure, a fast distance-based approach. We estimate cluster growth by focusing on recent cases in the Primary HIV Infection (PHI) stage. Results. The analyses reveal considerable overlap between cluster estimates obtained from conventional methods. The Gap Procedure and DM-PhyClus rely on different cluster definitions and as a result, suggest moderately different partitions. All estimates lead to similar conclusions about cluster expansion: several large clusters have experienced sizeable growth, and a few new transmission clusters are likely emerging. Conclusions. The lack of a gold standard measure for clustering quality makes picking a best estimate among those proposed difficult. Work aiming to refine clustering criteria would be required to improve estimates. Nevertheless, the results unanimously stress the role that clusters play in promoting HIV incidence among MSMs.

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arXiv2017

DM-PhyClus: A Bayesian phylogenetic algorithm for infectious disease transmission cluster inference

Background. Conventional phylogenetic clustering approaches rely on arbitrary cutpoints applied a posteriori to phylogenetic estimates. Although in practice, Bayesian and bootstrap-based clustering tend to lead to similar estimates, they often produce conflicting measures of confidence in clusters. The current study proposes a new Bayesian phylogenetic clustering algorithm, which we refer to as DM-PhyClus, that identifies sets of sequences resulting from quick transmission chains, thus yielding easily-interpretable clusters, without using any ad hoc distance or confidence requirement. Results. Simulations reveal that DM-PhyClus can outperform conventional clustering methods, as well as the Gap procedure, a pure distance-based algorithm, in terms of mean cluster recovery. We apply DM-PhyClus to a sample of real HIV-1 sequences, producing a set of clusters whose inference is in line with the conclusions of a previous thorough analysis. Conclusions. DM-PhyClus, by eliminating the need for cutpoints and producing sensible inference for cluster configurations, can facilitate transmission cluster detection. Future efforts to reduce incidence of infectious diseases, like HIV-1, will need reliable estimates of transmission clusters. It follows that algorithms like DM-PhyClus could serve to better inform public health strategies.

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