Category
Non-parametric statistics
Related publications (375)
Quantitative T2 Mapping of Acute Pancreatitis
Background: Quantification of the T2 signal by means of T2 mapping in acute pancreatitis (AP) has the potential to quantify the parenchymal edema. Quantitative T2 mapping may overcome the limitations of previously reported scoring systems for reliable asse ...
Hoboken2024Stability: a search for structure
In this thesis we study stability from several viewpoints. After covering the practical importance, the rich history and the ever-growing list of manifestations of stability, we study the following. (i) (Statistical identification of stable dynamical syste ...
EPFL2024Partial discharge localization in power transformer tanks using machine learning methods
Marcos Rubinstein, Hamidreza Karami
This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...
2024Novel theory and potential applications of central diastolic pressure decay time constant
Nikolaos Stergiopoulos, Georgios Rovas, Sokratis Anagnostopoulos, Vasiliki Bikia, Patrick Segers
Central aortic diastolic pressure decay time constant ( ) is according to the two-element Windkessel model equal to the product of total peripheral resistance (R) times total arterial compliance (C ). As such, it is related to arterial stiffness, which has ...
2024Weak correlations between visual abilities in healthy older adults, despite long-term performance stability
Michael Herzog, Simona Adele Garobbio
Using batteries of visual tests, most studies have found that there are only weak correlations between the performance levels of the tests. Factor analysis has confirmed these results. This means that a participant excelling in one test may rank low in ano ...
2024Assessment of past dioxin emissions from waste incineration plants based on archive studies and process modeling: a new methodological tool
Florian Frédéric Vincent Breider, Xiaocheng Zhang
Pollution from past industrial activities can remain unnoticed for years or even decades because the pollutant has only recently gained attention or identified by measurements. Modeling the emission history of pollution is essential for estimating populati ...
2024High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...
2024On distributional autoregression and iterated transportation
Victor Panaretos, Laya Ghodrati
We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...
Hoboken2024Influence of fiber orientation on the tensile behavior of UHPFRC and reinforced UHPFRC under static and fatigue loadings
Ultra-high performance fiber reinforced cementitious composite (UHPFRC) is a modern class of cementitious building materials. Because of its superior mechanical properties and durability, it is increasingly used globally to rehabilitate, strengthen and mod ...
EPFL2024Bayes-optimal Learning of Deep Random Networks of Extensive-width
Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui
We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...
2023