The power of monitoring: how to make the most of a contaminated multivariate sample
Diagnostic tools must rely on robust high-breakdown methodologies to avoid distortion in
the presence of contamination by outliers. However, a disadvantage of having a single, even …
the presence of contamination by outliers. However, a disadvantage of having a single, even …
Robust inference and modeling of mean and dispersion for generalized linear models
Abstract Generalized Linear Models (GLMs) are a popular class of regression models when
the responses follow a distribution in the exponential family. In real data the variability often …
the responses follow a distribution in the exponential family. In real data the variability often …
Neural networks to estimate generalized propensity scores for continuous treatment doses
Background: The generalized propensity score (GPS) addresses selection bias due to
observed confounding variables and provides a means to demonstrate causality of …
observed confounding variables and provides a means to demonstrate causality of …
With or Without a UN Mandate?: Exploring the Conflict Mitigating Abilities of Non-UN Peace Operations
A Wattman - 2022 - diva-portal.org
Non-UN peace operations are becoming an increasingly important conflict mitigating tool.
Whilst many studies find these operations unable to mitigate conflict and promote peace, the …
Whilst many studies find these operations unable to mitigate conflict and promote peace, the …
Robust and sparse statistical methods for actuarial sciences
This PhD thesis consists of two parts and in the first part, we focus on robust statistics. More
specifically, we consider robust regression when the response variable follows a distribution …
specifically, we consider robust regression when the response variable follows a distribution …