Results 111 to 120 of about 2,848,958 (338)

Pseudo likelihood and dimension reduction for data with nonignorable nonresponse

Statistical Theory and Related Fields, 2018
Tang et al. (2003. Analysis of multivariate missing data with nonignorable nonresponse. Biometrika, 90(4), 747–764) and Zhao & Shao (2015. Semiparametric pseudo-likelihoods in generalized linear models with nonignorable missing data.
Ji Chen, Bingying Xie, Jun Shao
doaj   +1 more source

Estimation of vegetation traits with kernel NDVI

Isprs Journal of Photogrammetry and Remote Sensing, 2023
Qiang Wang, Á. Moreno-Martínez, J. Muñoz-Marí, M. Campos-Taberner, Gustau Camps-Valls   +4 more
semanticscholar   +1 more source

Flexoelectrically Induced Polar Topology in Twisted SrTiO3 Membranes

Advanced Materials, EarlyView.
Twisted SrTiO3 bilayers host polar vortices of flexoelectric origin, revealed through combined experiment and theory. By reconstructing polarization from the toroidal moment of strain gradients, the work establishes a 3D chiral state with broken inversion and mirror symmetries.
Isabel Tenreiro, Hugo Aramberri, Víctor Rouco, Gabriel Sánchez‐Santolino, Mariona Cabero, Fernando Gallego, David Sánchez‐Manzano, José M. González‐Calbet, Federico Mompeán, Mar García‐Hernández, Alberto Rivera‐Calzada, Jorge Íñiguez‐González, Carlos León, Jacobo Santamaría   +13 more
wiley   +1 more source

Machine Learning–Assisted Bio‐Interfacial Engineering Resolves Structural–Functional Conflicts in Nanocomposites

Advanced Materials, EarlyView.
A machine learning‐guided bio‐interfacial design strategy resolves the long‐standing strength–toughness–functionality trade‐off in nanocomposites. By efficiently mapping high‐performance regions in the composition–processing space, the approach delivers hierarchically entangled, nanosheet‐pinned architectures that combine mechanical robustness ...
Hao Wang, Xianfeng Chen, Peiyao Yan, Siqi Liu, Biaobiao Yan, Junhua Kong, Siew Lang Teo, Kai Jin, Jie Zhang, Ping Koy Lam, Chaobin He   +10 more
wiley   +1 more source

Dimensionality reduction in kernel-based identification of Wiener system by cyclostationary excitations [PDF]

International Journal of Electronics and Telecommunications
The topic of nonparametric estimation of nonlinear characteristics in the Wiener system is examined. In this regard, the traditional kernel algorithm faces difficulties stemming from the dimensionality associated with the memory length of the dynamic ...
Gabriel Maik, Grzegorz Mzyk
doaj   +1 more source

Unfolded deep kernel estimation-attention UNet-based retinal image segmentation. [PDF]

Sci Rep, 2023
Radha K, Yepuganti K, Saritha S, Kamireddy C, Bavirisetti DP.   +4 more
europepmc   +1 more source

Nonparametric methods for volatility density estimation

, 2009
Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process.
Spreij, Peter, van Es, Bert, van Zanten, Harry   +2 more
core  

Self‐Powered Flexible Triboelectric‐Gated Ion‐Gel Transistor for Neuromorphic Tactile Sensing and Human Activity Recognition

Advanced Materials, EarlyView.
A fully flexible ion‐gel‐gated graphene‐channel transistor driven by a triboelectric nanogenerator enables self‐powered tactile sensing and synaptic learning. Mimicking spike‐rate‐dependent plasticity, the device exhibits frequency‐selective potentiation and depression, supporting rate‐coded neuromorphic computation even under flex.
Hanseong Cho, Seoyeon Park, Youngmin Lee, Sejoon Lee   +3 more
wiley   +1 more source

Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates

, 2019
We establish the first nonasymptotic error bounds for Kaplan-Meier-based nearest neighbor and kernel survival probability estimators where feature vectors reside in metric spaces.
Chen, George H.
core  

On Bootstrapping Kernel Spectral Estimates

The Annals of Statistics, 1992
This paper considers the problem of determining the statistical characteristics (such as probability distribution and confidence limits) of a kernel spectral density estimator, by using the bootstrap approach. A simple and natural bootstrapping scheme based on resampling the data periodogram ordinates (appropriately normalized) is introduced.
Franke, J., Hardle, W.
openaire   +2 more sources