bayesian-statistics

Optional stopping with Bayes factors: possibilities and limitations

Rianne de Heide / 2021-06-10

In recent years, a surprising number of scientific results have failed to hold up to continued scrutiny. Part of this ‘replicability crisis’ may be caused by practices that ignore the assumptions of traditional (frequentist) statistical methods (John, Loewenstein, and Prelec 2012). One of these…

spatiotemporal-data

Spatiotemporal modeling and real-time prediction of origin-destination traffic demand

Xiaochen Xian, Honghan Ye, Xin Wang and Kaibo Liu / 2021-06-09

Introduction In the past decades, intelligent transportation system (ITS) has brought advanced technology that enables a data-rich environment and unprecedented opportunities for traffic prediction, which is considered as one of the most prevalent issues facing ITS (Li et al., 2015). We discuss the…

webinars

Recent Advances in Functional Data Analysis

2021-04-29

The fourth “One World webinar” organized by YoungStatS will take place on June 30th, 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…

webinars

Composite-Based Structural Equation Modeling: Developments and Perspectives

2021-04-28

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…

machine-learning

A small step to understand Generative Adversarial Networks

Gérard Biau, Benoît Cadre, Maxime Sangnier and Ugo Tanielian / 2021-04-27

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…

hypothesis-testing

Generalizing the Neyman-Pearson Lemma for multiple hypothesis testing problems

Ruth Heller and Saharon Rosset / 2021-04-13

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…