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Jongmin Lee (Department of Statistics, Pusan National University)25/06/2026, 13:10
We formulate a general notion of intrinsic scale on metric spaces that encompasses the classical notion of dispersion on the real line. Moreover, we identify various robust examples such as intrinsic $\mathcal{S}_n$ and $\mathcal{Q}_n$ scales. Theoretical properties, including robustness, are established.
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Yvo Pokern (University College London)25/06/2026, 13:50
Electron-nuclear double resonance (ENDOR) spectroscopy is a sophisticated method to indirectly measure intra-molecular distances and orientations for complex biomolecules. Experimental limitations mean that the complex microwave echo signal, recorded as a real (in-phase) and imaginary (in-quadrature) component for each of the different frequencies used to excite the system, has an...
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Daisuke Kurisu (The University of Tokyo)25/06/2026, 14:50
In this talk, we propose geodesic difference-in-differences (GDID), a novel DID framework for outcomes in geodesic metric spaces, such as distributions, networks, and manifold-valued data. To address the absence of algebraic operations in these spaces, we use geodesics as proxies for differences and introduce the geodesic average treatment effect on the treated (ATT) as the causal estimand. We...
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Dr Shogo Kato (Institute of Statistical Mathematics)25/06/2026, 15:30
We propose a regression model in which the responses are spherical variables and the covariates include linear and/or spherical variables. A novel link function is introduced by extending the Möbius transformation on the sphere. This link function is an anisotropic mapping that enables scale control along each axis of the spherical covariates and for each linear covariate. It generalizes...
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Kipoong Kim (Changwon National University)25/06/2026, 16:20
Spherical observations arise in a variety of scientific disciplines, most notably in medical imaging, and yet the regression models for responses on the sphere remain relatively underdeveloped. A particular shortcoming of the existing literature lies in the absence of a rigorous framework for statistical inference. Many proposals emphasize model flexibility at the expense of theoretical...
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Prof. Yasuhito Tsuruta (Meiji University)25/06/2026, 17:00
Directional data consist of observations that lie on a unit sphere. Although the sample space is compact, outliers can still occur in practice, particularly when the data are highly concentrated. This motivates the development of robust estimation methods for directional models. Robust inference for directional data has therefore become a popular topic in recent statistical research. In this...
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Ian Dryden26/06/2026, 09:30
Object oriented data analysis (OODA) provides a framework for the statistical analysis of a wide variety of highly structured data. Shapes, images, functions, covariance matrices, networks and other manifold-valued data are all examples of object data that can be analyzed using OODA methods. Shapes of 3D objects are a type of manifold-valued data, invariant to translation, rotation and scale....
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Almond Stoecker (EPFL)26/06/2026, 10:50
The aim is to propose a novel nonlinear regression framework for responses taking values on a hypersphere. Rather than performing tangent space regression, where all the sphere responses are lifted to a single tangent space on which the regression is performed, we estimate conditional Fréchet means by minimizing squared distances on the nonlinear manifold. Yet, the tangent space serves as a...
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Ho Yun26/06/2026, 11:25
Comparing empirical subspaces of mismatched dimensions is a tricky problem that naturally arises in modern high-dimensional PCA, particularly within cross-validation and distributed learning frameworks. Traditional heuristics like the scree plot often yield ambiguous "elbows," leaving the true number of underlying signals up to guesswork.
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In this talk, I will present the asymptotic geometric... -
Mohsen Taheri Shalmani (University of Stavanger)26/06/2026, 13:30
Statistical shape analysis relies fundamentally on the establishment of correspondence and the definition of suitable shape representations to detect and explain local dissimilarities between groups of objects. Most existing approaches are based on extrinsic or noninvariant properties, requiring alignment and often leading to biased results. Moreover, providing a mathematical interpretation of...
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Zhigang Yao (National University of Singapore)26/06/2026, 14:10
The study of the interplay between statistics and geometry has expanded rapidly in both scope and influence over recent decades. The idea of manifold fitting can be traced back to the pioneering work of Hassler Whitney in the early 1930s. A fundamental question in this area is the following: given a data set, under what conditions can we construct a smooth (d)-dimensional surface (or manifold)...
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Jan Terje Kvaløy (University of Stavanger)26/06/2026, 14:50
In various settings goodness of fit tests are constructed based on processes which under a null hypothesis of interest converges to a Brownian bridge. Examples include classical testing of distributional assumptions, testing goodness of fit in time series models, finance model testing, choosing hyper parameters in machine learning and also testing for trend in recurrent event data. Tests are...
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Tetsuya Umino (Degree Programs in Pure and Applied Sciences, Graduate School of Science and Technology, University of Tsukuba, Ibaraki, Japan)26/06/2026, 15:50
High-dimensional, low-sample-size (HDLSS) data are now common in modern scientific fields, such as genomics and medical imaging, where the number of variables significantly exceeds the number of observations. In this talk, we consider the estimation of linear combinations of high-dimensional mean vectors in multi-sample settings. Such inference is a fundamental component of various...
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Jaesung Park (Seoul National University)26/06/2026, 16:15
This paper introduces a novel extension of Fréchet means, referred to as generalized Fréchet means, as a comprehensive framework for describing the characteristics of random elements. The generalized Fréchet mean is defined as the minimizer of a cost function, and the framework encompasses various extensions of Fréchet means that have appeared in the literature. The most distinctive feature of...
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Siwon Kim (Seoul National University)26/06/2026, 16:40
Information centrality is a vertex measure of a graph, which is a harmonic mean of effective conductances defined between a given vertex and all other connected vertices. As the reciprocal of effective resistance, effective conductance serves as a higher-order connectivity measure and has a connection with a renowned mathematical data analysis technique, spectral embedding, by the graph...
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Jan Zavadil (UiT)26/06/2026, 17:05
Mammography screening produces multiple images per patient, each assessed for cancer risk and breast density using the BI-RADS system. While these tasks are clinically related, density both increases cancer risk and reduces imaging sensitivity, multi-task learning models do not consistently outperform single-task baselines. This raises a natural question: how much information is truly shared...
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Jae-Hun Jung (POSTECH)27/06/2026, 09:30
Abstract: The shape of complex data -- time series, functional observations, and multimodal measurements -- encodes structural information that classical statistical methods often fail to capture. Topological data analysis addresses this through persistent homology, which tracks the evolution of topological features across a filtration of the data. However, the outcome of any topological...
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Keunsu Kim27/06/2026, 10:50
Nonnegative Matrix Factorization (NMF) is a core tool for uncovering the latent structure of data. Since basis vectors encapsulate the essential features of the data, a central objective of this study is to learn them in a way that is both interpretable and structurally meaningful. In this talk, we quantify the intuition of what constitutes a “good basis” using persistent homology, and...
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JEONG MIN JEON (Seoul National University)27/06/2026, 11:30
In this work, we introduce density estimation on a general Lie group when data contain measurement errors and the distribution of measurement error is unknown. We estimate the target density without additional observations, such as an observable random sample from the measurement error distribution or repeated measurements. To achieve this, we take a semiparametric approach assuming that the...
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