Multiple comparisons procedures for complex dependent data with application to study human brain complex network properties

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Diffusion Magnetic Resonance Imaging (dMRI) is a powerful non-invasive method for studying white matter tracts of the brain. However, accurate microstructure estimation with fiber orientation distribution (FOD) using existing computational methods requires ...

Context. We present a novel approach to the construction of mock galaxy catalogues for large-scale structure analysis based on the distribution of dark matter halos obtained with effective bias models at the field level. Aims. We aim to produce mock galaxy ...

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio R between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, and the decay rate of gravitational ...

This work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate inf ...

In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents. In this setup, each agent receives data that might be generated from a different hypothesis (or state) than the data other agents receive. In c ...

We present the La Mobiliere insurance customers dataset: a 12-year-long longitudinal collection of data on policies of customers of the Swiss insurance company La Mobiliere. To preserve the privacy of La Mobiliere customers, we propose the data aggregated ...

The objective of this study was to examine if patterns of resting-state brain activity and functional connectivity in cortical and subcortical regions in patients with early symptomatic amyotrophic lateral sclerosis (ALS) resemble those of behavioural vari ...

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

\alpha

. Two ...

Objectives To determine and compare the qualitative and quantitative diagnostic performance of a single sagittal fast spin echo (FSE) T2-weighted Dixon sequence in differentiating benign and malignant vertebral compression fractures (VCF), using multiple r ...

Algorithms are now routinely used to make consequential decisions that affect human lives. Examples include college admissions, medical interventions or law enforcement. While algorithms empower us to harness all information hidden in vast amounts of data, ...

Null models are useful for assessing whether a dataset exhibits a non-trivial property of interest. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fM ...

We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven d ...

Digital images enable quantitative analysis of material properties at micro and macro length scales, but choosing an appropriate resolution when acquiring the image is challenging. A high resolution means longer image acquisition and larger data requiremen ...

Embedded and cyber-physical systems are pervading all aspects of our lives, including sensitive and critical ones. As a result, they are an alluring target for cyber attacks. These systems, whose implementation is often based on reconfigurable hardware, ar ...

There has been growing interest in studying connections between generalization error of learning algorithms and information measures. In this work, we generalize a result that employs the maximal leakage, a measure of leakage of information, and explore ho ...

During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a better understanding of large random matrices. These advances have enabled interesting applications in the domain of communication. Although this theory can ...

Cerebrospinal fluid (CSF) and white matter (WM) signal suppression techniques allow better visualization of both WM and gray matter (GM) lesions in such disorders as multiple sclerosis and epilepsy. Recently, a technique, FLuid And White matter Suppression ...

Kinetic models of metabolism can be constructed to predict cellular regulation and devise metabolic engineering strategies, and various promising computational workflows have been developed in recent years for this. Due to the uncertainty in the kinetic pa ...

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover th ...

In this thesis, we deal with one of the facets of the statistical detection problem. We study a particular type of alternative, the mixture model. We consider testing where the null hypothesis corresponds to the absence of a signal, represented by some kno ...

Multiple comparisons procedures for complex dependent data with application to study human brain complex network properties

Chattez avec Graph Search

Robust Estimation of the Microstructure of the Early Developing Brain Using Deep Learning

DESI mock challenge Halo and galaxy catalogues with the bias assignment method

An Unbiased Method of Measuring the Ratio of Two Data Sets

Partial Information Sharing Over Social Learning Networks

Social Learning with Disparate Hypotheses

High spatial resolution dataset of La Mobiliere insurance customers

Cortical and subcortical changes in resting-state neuronal activity and connectivity in early symptomatic ALS and advanced frontotemporal dementia

Multiple testing with test statistics following heavy-tailed distributions

Differentiation between benign and malignant vertebral compression fractures using qualitative and quantitative analysis of a single fast spin echo T2-weighted Dixon sequence

A Statistical Test for Probabilistic Fairness

Interpreting null models of resting-state functional MRI dynamics: not throwing the model out with the hypothesis

Distributionally Robust Optimization with Markovian Data

The Impact of Changes in Resolution on the Persistent Homology of Images

Built-in Self-Evaluation of First-Order Power Side-Channel Leakage for FPGAs

Learning and Adaptive Data Analysis via Maximal Leakage

Statistical Applications of Random Matrix Theory: Comparison of Two Populations

Advantages of fluid and white matter suppression (FLAWS) with MP2RAGE compared with double inversion recovery turbo spin echo (DIR-TSE) at 7T

Statistical inference in ensemble modeling of cellular metabolism

Statistical Aspects of Wasserstein Distances

The detection of two-component mixture alternatives : Theory and methods