Results 151 to 160 of about 4,792,820 (293)
Optimal Penalized Function-on-Function Regression under a Reproducing Kernel Hilbert Space Framework. [PDF]
Sun X, Du P, Wang X, Ma P.
europepmc +1 more source
On Metric Choice in Dimension Reduction for Fréchet Regression
Summary Fréchet regression is becoming a mainstay in modern data analysis for analysing non‐traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data.
Abdul‐Nasah Soale +3 more
wiley +1 more source
Hilbert Space Delocalization under Random Unitary Circuits. [PDF]
Turkeshi X, Sierant P.
europepmc +1 more source
Hájek-Rényi inequality for m-asymptotically almost negatively associated random vectors in Hilbert space and applications. [PDF]
Ko MH.
europepmc +1 more source
Summary Distance covariance is a popular measure of dependence between random variables. It has some robustness properties, but not all. We prove that the influence function of the usual distance covariance is bounded, but that its breakdown value is zero.
Sarah Leyder +2 more
wiley +1 more source
Navigating the 16-dimensional Hilbert space of a high-spin donor qudit with electric and magnetic fields. [PDF]
Fernández de Fuentes I +13 more
europepmc +1 more source
ABSTRACT Slow waves and sleep spindles characterise non‐rapid eye movement (NREM) sleep and support cognitive and plasticity‐related functions. While their stability across nights is well established, less is known about their consistency across daytime naps.
Damiana Bergamo +3 more
wiley +1 more source
Weak convergence theorem for a class of split variational inequality problems and applications in a Hilbert space. [PDF]
Tian M, Jiang BN.
europepmc +1 more source
ABSTRACT Machine‐learning‐based sleep staging models have achieved expert‐level performance on standard polysomnographic (PSG) data. However, their application to EEG recorded by wearable devices remains limited by non‐conventional referencing montage and the lack of benchmarking against PSG.
Federico Salfi +6 more
wiley +1 more source
Monitoring panels of sparse functional data
Panels of random functions are common in applications of functional data analysis. They often occur when sequences of functions are observed at a number of different locations. We propose a methodology to monitor for structural breaks in such panels and to identify the changing components with statistical certainty.
Tim Kutta +2 more
wiley +1 more source

