Publications by YoungStatS
Spatiotemporal modeling and real-time prediction of origin-destination traffic demand
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 online prediction of the origin-destination (OD)...
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Optional stopping with Bayes factors: possibilities and limitations
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 assumptions is that the experimental protocol sh...
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Optional stopping with Bayes factors: possibilities and limitations
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 assumptions is that the experimental protocol sh...
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Concentration Inequalities in Machine Learning
The fifth “One World webinar” organized by YoungStatS will take place on September 15th, 2021. Selected young European researchers active in the areas of probability and machine learning will present their recent contributions. The webinar is joint cooperation between the Young Researchers Committee of the Bernoulli Society and the YoungStatS...
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Depth Quantile Functions
Figure 1: Depth quantile functions for the wine data (d=13), class 2 vs class 3. Blue curves correspond to between class comparisons, red/pink correspond to within class comparisons. A common technique in modern statistics is the so-called kernel trick, where data is mapped into a (usually) infinite-dimensional feature space, where various statis...
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Advancements in Symbolic Data Analysis
Advancements in Symbolic Data Analysis The sixth “One World webinar” organized by YoungStatS will take place on October 27th, 2021. With the development of digital systems, very large datasets have become routine. However, standard statistical approaches do not have the power or flexibility to analyse these efficiently, and extract the requi...
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Advances in Difference-in-Differences in Econometrics
Advances in Difference-in-Differences in Econometrics The eighth “One World webinar” organized by YoungStatS will take place on December 13th, 2021. The difference-in-differences design is a quasi-experimental identification strategy for estimating causal effects which has become the single most popular research design in the quantitative so...
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Optimal disclosure risk assessment
Disclosure risk for microdata Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample \({X}_n=(X_{1},\ldots,X_{n})\) of size \(n\) from a finite population of size \(\bar{n}=n+\lambda n\), with \(\lambda>0\), such that each sample record \(X_i\) contains tw...
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Novel Algebraic Approaches to Maximum Likelihood Estimation
Novel Algebraic Approaches to Maximum Likelihood Estimation The seventh “One World webinar” organized by YoungStatS will take place on November 17th, 2021. Maximum likelihood estimation (MLE) is a tool in data analysis to estimate a probability distribution or density in a statistical model for given data. In recent decades, algebraic and co...
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Frozen percolation on the binary tree is nonendogenous
In frozen percolation on a graph, there is a barrier located on each edge. Initially, the barriers are closed and they are assigned i.i.d. uniformly distributed activation times. At its activation time, a barrier opens, provided it is not frozen. At a fixed set \(\Xi\) of freezing times, all barriers that percolate are frozen. In particular, if ...
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