arXiv subjects

Valérie Ventura

Publications and source records attributed to Valérie Ventura.

6 recordsLinked to original sources

arXiv2026

Causal Inference Using Augmented Epidemic Models

Epidemic models describe the evolution of a communicable disease over time. These models are often modified to include the effects of interventions (control measures) such as vaccination, social distancing, school closings etc. Many such models were proposed during the COVID-19 epidemic. Inevitably these models are used to answer the question: What is the effect of the intervention on the epidemic? These models can either be interpreted as data generating models describing observed random variables or as causal models for counterfactual random variables. These two interpretations are often conflated in the literature. We discuss the difference between these two types of models, and then we discuss how to estimate the parameters of the model. Our focus is causal inference for parameters in epidemic models by adjusting for confounders, allowing time varying interventions.

stat.ME↗

arXiv2025

Cross-Population Amplitude Coupling in High-Dimensional Oscillatory Neural Time Series

Neural oscillations have long been considered important markers of interaction across brain regions, yet identifying coordinated oscillatory activity from high-dimensional multiple-electrode recordings remains challenging. We sought to quantify time-varying covariation of oscillatory amplitudes across two brain regions, during a memory task, based on local field potentials recorded from 96 electrodes in each region. We extended Canonical Correlation Analysis (CCA) to multiple time series through the cross-correlation of latent time series. This, however, introduces a large number of possible lead-lag cross-correlations across the two regions. To manage that high dimensionality we developed rigorous statistical procedures aimed at finding a small number of dominant lead-lag effects. The method correctly identified ground truth structure in realistic simulation-based settings. When we used it to analyze local field potentials recorded from prefrontal cortex and visual area V4 we obtained highly plausible results. The new statistical methodology could also be applied to other slowly-varying high-dimensional time series.

stat.ME↗

arXiv2025

Robust Simulation Based Inference

Simulation-Based Inference (SBI) is an approach to statistical inference where simulations from an assumed model are used to construct estimators and confidence sets. SBI is often used when the likelihood is intractable and to construct confidence sets that do not rely on asymptotic methods or regularity conditions. Traditional SBI methods assume that the model is correct, but, as always, this can lead to invalid inference when the model is misspecified. This paper introduces robust methods that allow for valid frequentist inference in the presence of model misspecification. We propose a framework where the target of inference is a projection parameter that minimizes a discrepancy between the true distribution and the assumed model. The method guarantees valid inference, even when the model is incorrectly specified and even if the standard regularity conditions fail. Alternatively, we introduce model expansion through exponential tilting as another way to account for model misspecification. We also develop an SBI based goodness-of-fit test to detect model misspecification. Finally, we propose two ideas that are useful in the SBI framework beyond robust inference: an SBI based method to obtain closed form approximations of intractable models and an active learning approach to more efficiently sample the parameter space.

stat.ME↗

arXiv2024

Frequentist Inference for Semi-mechanistic Epidemic Models with Interventions

The effect of public health interventions on an epidemic are often estimated by adding the intervention to epidemic models. During the Covid-19 epidemic, numerous papers used such methods for making scenario predictions. The majority of these papers use Bayesian methods to estimate the parameters of the model. In this paper we show how to use frequentist methods for estimating these effects which avoids having to specify prior distributions. We also use model-free shrinkage methods to improve estimation when there are many different geographic regions. This allows us to borrow strength from different regions while still getting confidence intervals with correct coverage and without having to specify a hierarchical model. Throughout, we focus on a semi-mechanistic model which provides a simple, tractable alternative to compartmental methods.

stat.ME↗

arXiv2018

Computationally efficient model selection for joint spikes and waveforms decoding

A recent paradigm for decoding behavioral variables or stimuli from neuron ensembles relies on joint models for electrode spike trains and their waveforms, which, in principle, is more efficient than decoding from electrode spike trains alone or from sorted neuron spike trains. In this paper, we decode the velocity of arm reaches of a rhesus macaque monkey to show that including waveform features indiscriminately in a joint decoding model can contribute more noise and bias than useful information about the kinematics, and thus degrade decoding performance. We also show that selecting which waveform features should enter the model to lower the prediction risk can boost decoding performance substantially. For the data analyzed here, a stepwise search for a low risk electrode spikes and waveforms joint model yielded a low risk Bayesian model that is 30% more efficient than the corresponding risk minimized Bayesian model based on electrode spike trains alone. The joint model was also comparably efficient to decoding from a risk minimized model based only on sorted neuron spike trains and hash, confirming previous results that one can do away with the problematic spike sorting step in decoding applications. We were able to search for low risk joint models through a large model space thanks to a short cut formula, which accelerates large matrix inversions in stepwise searches for models based on Gaussian linear observation equations.

stat.AP↗

arXiv2015

Detecting changes in maps of gamma spectra with Kolmogorov-Smirnov tests

Various security, regulatory, and consequence management agencies are interested in continuously monitoring wide areas for unexpected changes in radioactivity. Existing detection systems are designed to search for radioactive sources but are not suited to repeat mapping and change detection. Using a set of daily spectral observations collected at the Pickle Research Campus, we improved on the prior Spectral Comparison Ratio Anomaly Mapping (SCRAM) algorithm and developed a new method based on two-sample Kolmogorov-Smirnov tests to detect sudden spectral changes. We also designed simulations and visualizations of statistical power to compare methods and guide deployment scenarios.

physics.data-an↗