Publications by YoungStatS
Recent Advances in COVID-19 modelling
YoungStatS project of Young Statisticians Europe, FENStatS, proudly announces our first One World YoungStatS webinar. With four young scholars, we will discuss Recent Advances in the Modelling of COVID-19, presenting novel statistical models, interesting advancements and applications of mechanistic models, as well as their combinations. When: Wed...
1827 sym
Higher Order Targeted Maximum Likelihood Estimation
Summary We propose a higher order targeted maximum likelihood estimation (TMLE) that only relies on a sequentially and recursively defined set of data-adaptive fluctuations. Without the need to assume the often too stringent higher order pathwise differentiability, the method is practical for implementation and has the potential to be fully compu...
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Compositional scalar-on-function regression as a tool (not only) for geological data
Compositional data are characterized by the fact that the relevant information is contained not necessarily in the absolute values but rather in the relative proportions between particular components. As an example, take household expenditures for different purposes (housing, groceries, travel etc.) or geochemical composition of a certain soil sa...
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A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models
In the toolbox of most scientists over the past century, there have been few methods as powerful and as versatile as linear regression. The introduction of the generalized linear model (GLM) framework in the 1970’s extended the inferential and predictive capabilities to binary or count data. While the effect of this ‘Swiss Army knife’ of sc...
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Analysis of a Two-Layer Neural Network via Displacement Convexity
Abstract We consider the problem of learning a function defined on a compact domain, using linear combinations of a large number of “bump-like” components (neurons). This idea lies at the core of a variety of methods from two-layer neural networks to kernel regression, to boosting. In general, the resulting risk minimization problem is non-co...
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Developments in Bayesian Nonparametrics
The second “One World webinar” organized by YoungStatS will take place on April 21st. The focus of this webinar will be on illustrating modern advances in Bayesian Nonparametrics data analysis, discussing challenging theoretical problems and stimulating case-studies within this active area of research. When & Where: Wednesday, April 21st, 16...
1526 sym
Generalizing the Neyman-Pearson Lemma for multiple hypothesis testing problems
Introduction Let us start by considering the optimal rejection policy for a single hypothesis testing problem. There are three elements to the problem. The objective: to maximize the power to reject the null hypothesis; The constraint: to control the type I error probability, so that it is at most a predefined \(\alpha\); The decision policy: for...
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A small step to understand Generative Adversarial Networks
Introduction In the last decade, there have been spectacular advances on the practical side of machine learning. One of the most impressive may be the success of Generative Adversarial Networks (GANs) for image generation (Goodfellow et al. 2014). State of the art models are capable of producing portraits of fake persons that look perfectly authe...
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Composite-Based Structural Equation Modeling: Developments and Perspectives
The third “One World webinar” organized by YoungStatS will take place on May 19th, 2021. The focus of this webinar will be on composite-based structural equation modeling, particularly on partial least squares path modeling (Wold, 1982; Lohmöller, 1989) and approaches to assess composite models. The webinar will present some of the most inte...
1616 sym
Recent Advances in Functional Data Analysis
The fourth “One World webinar” organized by YoungStatS will take place on June 20th, 2021. The topic of this webinar is on Functional Data Analysis. Selected young European researchers active in this area of research will present their contributions on spherical functional autoregressions, additive models, and clustering methods for functiona...
1729 sym