Recent Progress in Polymorphism-Based Population Genetic Inference

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Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

Long-term consumption of lipid-rich foods can contribute to common metabolic diseases and systemic low-grade inflammation. However, dietary responses and the development of non-communicable diseases are shaped by genetic factors and gene-by-environment int ...

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 ...

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

We summarize what we consider to be the two main limitations of the "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event" (Schmidli et al. 2022). First, the authors did not give detailed guidance on how to choose an appropriate esti ...

In this paper we propose a Monte Carlo maximum likelihood estimation strategy for discretely observed Wright–Fisher diffusions. Our approach provides an unbiased estimator of the likelihood function and is based on exact simulation techniques that are of s ...

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

The terminology of neurological disorders encompasses a range of serious illnesses (e.g., epilepsy, Alzheimer's disease) leading to morbidity, disability, and stigma. Epilepsy alone affects over 50 million people worldwide, and these figures are rising as ...

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This paper presents the experimental validation of a linear recursive state estimation (SE) process for hybrid AC/DC microgrids proposed in the authors' previous work. The SE uses a unified and linear measurement model that relies on the use of synchronize ...

This thesis presents work at the junction of statistics and climate science. We first provide methodology for use by climate scientists when performing fast event attribution using extreme value theory, and then describe two interdisciplinary projects in c ...

As the world’s population grows in cities, urban dwellers spend a large amount of time inside their home, making housing health ever more important. Critical for residents’ health, the interactions between indoor residential environments and outdoor enviro ...

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...

An artificial floods program has been implemented on the Sarine residual flow reach, downstream of the Rossens dam. Two artificial floods were released in 2016 (coupled with sediment augmentation) and in 2020. A natural flood occurred in 2021. The indicato ...

Recent Progress in Polymorphism-Based Population Genetic Inference

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Robust discrete choice models with t-distributed kernel errors

Identifying genetic and dietary modulators of metabolic disorders using systems genetics

An Unbiased Method of Measuring the Ratio of Two Data Sets

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Timing and spatial selection bias in rapid extreme event attribution

We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event"

Unbiased Likelihood Estimation of Wright–Fisher Diffusion Processes

Stochastic adaptive resampling for the estimation of discrete choice models

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Real-time Personalized Monitoring of Neurological Disorders on Wearable Systems

State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking

Experimental Validation of a Unified and Linear State Estimation Method for Hybrid AC/DC Microgrids

Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

Housing, street and health: a new systemic research framework

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

Effects of water releases and sediment supply on a residual flow reach