Publications related to On null hypotheses in survival analysis

Transportation-based functional ANOVA and PCA for covariance operators

Victor Panaretos, Yoav Zemel, Valentina Masarotto

We consider the problem of comparing several samples of stochastic processes with respect to their second-order structure, and describing the main modes of variation in this second order structure, if present. These tasks can be seen as an Analysis of Vari ...

Inst Mathematical Statistics-Ims2024

Partial Information Sharing Over Social Learning Networks

Ali H. Sayed, Virginia Bordignon

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Social Learning with Disparate Hypotheses

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos

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 ...

IEEE2022

Learning new physics from an imperfect machine

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We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties a ...

SPRINGER2022

Multiple testing with test statistics following heavy-tailed distributions

Zhiwen Jiang

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

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EPFL2021

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

Dimitri Nestor Alice Van De Ville, Raphaël Pierre Liégeois

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 ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2021

A Statistical Test for Probabilistic Fairness

Daniel Kuhn, Viet Anh Nguyen, Bahar Taskesen

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, ...

2021

Probabilistic and Bayesian methods for uncertainty quantification of deterministic and stochastic differential equations

Giacomo Garegnani

In this thesis we explore uncertainty quantification of forward and inverse problems involving differential equations. Differential equations are widely employed for modeling natural and social phenomena, with applications in engineering, chemistry, meteor ...

EPFL2021

A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization

Ali H. Sayed, Emre Telatar, Mert Kayaalp, Yunus Inan

We consider a distributed binary hypothesis testing setup where multiple nodes send quantized information to a central processor, which is oblivious to the nodes’ statistics. We study the regime where the type-II error decays exponentially and the type-I e ...

2021

Dynamical System Modeling of Self-Regulated Systems Undergoing Multiple Excitations: First Order Differential Equation Approach

Julien Gilles Edgar Gateau

This article proposes a dynamical system modeling approach for the analysis of longitudinal data of self-regulated homeostatic systems experiencing multiple excitations. It focuses on the evolution of a signal (e.g., heart rate) before, during, and after e ...

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD2020

On null hypotheses in survival analysis

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Transportation-based functional ANOVA and PCA for covariance operators

Partial Information Sharing Over Social Learning Networks

Social Learning with Disparate Hypotheses

Learning new physics from an imperfect machine

Multiple testing with test statistics following heavy-tailed distributions

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

A Statistical Test for Probabilistic Fairness

Probabilistic and Bayesian methods for uncertainty quantification of deterministic and stochastic differential equations

A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization

Dynamical System Modeling of Self-Regulated Systems Undergoing Multiple Excitations: First Order Differential Equation Approach

Social Learning with Disparate Hypotheses

Transportation-based functional ANOVA and PCA for covariance operators

Partial Information Sharing Over Social Learning Networks

Multiple testing with test statistics following heavy-tailed distributions

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

Learning new physics from an imperfect machine

A Statistical Test for Probabilistic Fairness

Probabilistic and Bayesian methods for uncertainty quantification of deterministic and stochastic differential equations

Dynamical System Modeling of Self-Regulated Systems Undergoing Multiple Excitations: First Order Differential Equation Approach

A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization