Nonlinear functional regression by functional deep neural network with kernel embedding
With the rapid development of deep learning in various fields of science and technology,
such as speech recognition, image classification, and natural language processing, recently …
such as speech recognition, image classification, and natural language processing, recently …
[HTML][HTML] Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses
M Mądziel - Energies, 2024 - mdpi.com
This paper presents the process of creating a model for electric vehicle (EV) energy
consumption, enabling the rapid generation of results and the creation of energy maps. The …
consumption, enabling the rapid generation of results and the creation of energy maps. The …
[HTML][HTML] Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities
Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon
emissions and promoting environmental sustainability in the automotive industry. However …
emissions and promoting environmental sustainability in the automotive industry. However …
Growing-dimensional partially functional linear models: non-asymptotic optimal prediction error
H Zhang, X Lei - Physica Scripta, 2023 - iopscience.iop.org
Under the reproducing kernel Hilbert spaces (RKHS), we focus on the penalized least-
squares of the partially functional linear models (PFLM), whose predictor contains both …
squares of the partially functional linear models (PFLM), whose predictor contains both …
Functional data learning using convolutional neural networks
J Galarza, T Oraby - Machine Learning: Science and Technology, 2024 - iopscience.iop.org
In this paper, we show how convolutional neural networks (CNNs) can be used in
regression and classification learning problems for noisy and non-noisy functional data (FD) …
regression and classification learning problems for noisy and non-noisy functional data (FD) …
Two-sample inference for sparse functional data
Q Wang - 2021 - projecteuclid.org
In this paper, we develop an asymptotic χ 2 test for detecting differences among mean
functions when sparse and irregular observations are drawn from the underlying continuous …
functions when sparse and irregular observations are drawn from the underlying continuous …
Non-asymptotic optimal prediction error for growing-dimensional partially functional linear models
H Zhang, X Lei - arXiv preprint arXiv:2009.04729, 2020 - arxiv.org
Under the reproducing kernel Hilbert spaces (RKHS), we consider the penalized least-
squares of the partially functional linear models (PFLM), whose predictor contains both …
squares of the partially functional linear models (PFLM), whose predictor contains both …
Conformal inference for random objects
H Zhou, HG Müller - arXiv preprint arXiv:2405.00294, 2024 - arxiv.org
We develop an inferential toolkit for analyzing object-valued responses, which correspond to
data situated in general metric spaces, paired with Euclidean predictors within the conformal …
data situated in general metric spaces, paired with Euclidean predictors within the conformal …
Linearized maximum rank correlation estimation when covariates are functional
W Xu, X Zhang, H Liang - Journal of Multivariate Analysis, 2024 - Elsevier
This paper extends the linearized maximum rank correlation (LMRC) estimation proposed
by Shen et al.(2023) to the setting where the covariate is a function. However, this extension …
by Shen et al.(2023) to the setting where the covariate is a function. However, this extension …
Change point localisation and inference in fragmented functional data
We study the problem of change point localisation and inference for sequentially collected
fragmented functional data, where each curve is observed only over discrete grids randomly …
fragmented functional data, where each curve is observed only over discrete grids randomly …